Environmental Modeling & Assessment

, Volume 21, Issue 4, pp 531–546 | Cite as

Support Vector Machine Modeling Using Particle Swarm Optimization Approach for the Retrieval of Atmospheric Ammonia Concentrations

  • Jiawei Zhang
  • Frank K. Tittel
  • Longwen Gong
  • Rafal Lewicki
  • Robert J. Griffin
  • Wenzhe Jiang
  • Bin Jiang
  • Mingbao Li
Article
  • 247 Downloads

Abstract

This study was performed in order to improve the estimation accuracy of atmospheric ammonia (NH3) concentration levels in the Greater Houston area during extended sampling periods. The approach is based on selecting the appropriate penalty coefficient C and kernel parameter σ2. These parameters directly influence the regression accuracy of the support vector machine (SVM) model. In this paper, two artificial intelligence techniques, particle swarm optimization (PSO) and a genetic algorithm (GA), were used to optimize the SVM model parameters. Data regarding meteorological variables (e.g., ambient temperature and wind direction) and the NH3 concentration levels were employed to develop our two models. The simulation results indicate that both PSO-SVM and GA-SVM methods are effective tools to model the NH3 concentration levels and can yield good prediction performance based on statistical evaluation criteria. PSO-SVM provides higher retrieval accuracy and faster running speed than GA-SVM. In addition, we used the PSO-SVM technique to estimate 17 drop-off NH3 concentration values. We obtained forecasting results with good fitting characteristics to a measured curve. This proved that PSO-SVM is an effective method for estimating unavailable NH3 concentration data at 3, 4, 5, and 6 parts per billion (ppb), respectively. A 4-ppb NH3 concentration had the optimum prediction performance of the simulation results. These results showed that the selection of the set-point values is a significant factor in compensating for the atmospheric NH3 dropout data with the PSO-SVM method. This modeling approach will be useful in the continuous assessment of NH3 sensor discrete data sources.

Keywords

Atmospheric ammonia concentration Predictions of concentration levels Particle swarm optimization Support vector machine 

References

  1. 1.
    Agirre-Basurko, E., Ibarra-Berastegi, G., & Madariaga, I. (2006). Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environmental Modelling and Software, 21(4), 430–446.CrossRefGoogle Scholar
  2. 2.
    Al-Alawi, S. M., Abdul-Wahab, S. A., & Bakheit, C. S. (2008). Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environmental Modelling and Software, 23(4), 396–403.CrossRefGoogle Scholar
  3. 3.
    AlRashidi, M. R., & EL-Naggar, K. M. (2010). Long term electric load forecasting based on particle swarm optimization. Applied Energy, 87(1), 320–326.CrossRefGoogle Scholar
  4. 4.
    Anandhi, A., Srinivas, V. V., Nanjundiah, R., & Kumar, N. D. (2008). Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. International Journal of Climatology, 28(3), 401–420.CrossRefGoogle Scholar
  5. 5.
    Aneja, V. P., Bunton, B., Walker, J. T., & Malik, B. P. (2001). Measurement and analysis of atmospheric ammonia emissions from anaerobic lagoons. Atmospheric Environment, 35(11), 1949–1958.CrossRefGoogle Scholar
  6. 6.
    Antanasijević, D., Pocajt, V., Povrenović, D., Perić-Grujić, A., & Ristić, M. (2013). Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environmental Science and Pollution Research, 20(12), 9006–9013.CrossRefGoogle Scholar
  7. 7.
    Ayat, N. E., Cheriet, M., & Suen, C. Y. (2005). Automatic model selection for the optimization of SVM kernels. Pattern Recognition, 38(10), 1733–1745.CrossRefGoogle Scholar
  8. 8.
    Berastegi, G. I., Elias, A., Barona, A., Saenz, J., Ezcurra, A., & Argandoña, J. D. (2008). From diagnosis to prognosis for forecasting air pollution using neural networks: air pollution monitoring in Bilbao. Environmental Modelling & Software, 23(5), 622–637.CrossRefGoogle Scholar
  9. 9.
    Blatter, A., Neftel, A., Dasgupta, P. K., & Simon, P. K. (1994). A combined wet effluent denuder and mist chamber system for deposition measurements of NH3, NH4, HNO-3 and NO3. In G. Angeletti & G. Restelli (Eds.), Physicochemical behaviour of atmospheric pollutants (pp. 767–772). Brussels: European Commission.Google Scholar
  10. 10.
    Bobrutzki, K. V., Braban, C. F., Famulari, D., Jones, S. K., Blackall, T., Smith, T. E. L., Blom, M., Coe, H., Gallagher, M., Ghalaieny, M., McGillen, M. R., Percival, C. J., Whitehead, J. D., Ellis, R., Murphy, J., Mohacsi, A., Pogany, A., Junninen, H., Rantanen, S., Sutton, M. A., & Nemitz, E. (2010). Field inter-comparison of eleven atmospheric ammonia measurement techniques. Atmospheric Measurement Techniques, 3, 91–112.CrossRefGoogle Scholar
  11. 11.
    Boniecki, P., Dach, J., Pilarski, K., & Piekarska-Boniecka, H. (2012). Artificial neural networks for modeling ammonia emissions released from sewage sludge composting. Atmospheric Environment, 57(9), 49–54.CrossRefGoogle Scholar
  12. 12.
    Bray, M., & Han, D. (2004). Identification of support vector machines for runoff modeling. Journal of Hydroinformatics, 6, 265–280.Google Scholar
  13. 13.
    Breban, S., Saudemont, C., Vieillard, S., & Robyns, B. (2013). Experimental design and genetic algorithm optimization of a fuzzy-logic supervisor for embedded electrical power systems. Mathematics and Computers in Simulation, 91(5), 91–107.CrossRefGoogle Scholar
  14. 14.
    Caldas, L. G., & Norford, L. K. (2002). A design optimization tool based on a genetic algorithm. Automation in Construction, 11(2), 173–184.CrossRefGoogle Scholar
  15. 15.
    Chelani, A. B., Chalapati Rao, C. V., Phadke, K. M., & Hasan, M. Z. (2002). Prediction of sulphur dioxide concentration using artificial neural networks. Environmental Modelling and Software, 17(2), 159–166.CrossRefGoogle Scholar
  16. 16.
    Cherkassky, V., & Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks, 17(1), 113–126.CrossRefGoogle Scholar
  17. 17.
    Cimen, M. (2008). Estimation of daily suspended sediments using support vector machines. Hydrological Sciences Journal, 53(3), 656–666.CrossRefGoogle Scholar
  18. 18.
    Clarisse, L. D., Hurtmans, A. J., Prata, F., Karagulian, C., Clerbaux, M. D., & Mazière, P. F. (2010). Retrieving radius, concentration, optical depth, and mass of different types of aerosols from high-resolution infrared nadir spectra. Applied Optic, 49(19), 3713–3722.CrossRefGoogle Scholar
  19. 19.
    Dutot, A. L., Rynkiewicz, J., Steiner, F. E., & Rude, J. (2007). A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions. Environmental Modelling & Software, 22(9), 1261–1269.CrossRefGoogle Scholar
  20. 20.
    Erisman, J. W., Hensen, A., Otjes, R., Jongejan, P., Moels, H., Slanina, J., Khlystov, A., & Bulk, P. v. (2001). Instrument development and application in studies and monitoring of ambient ammonia. Atmospheric Environment, 35, 1913–1922.CrossRefGoogle Scholar
  21. 21.
    Fei, S. W., Wang, M. J., Miao, Y. B., Tu, J., & Liu, C. L. (2009). Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil. Energy Conversion and Management, 50, 1604–1609.CrossRefGoogle Scholar
  22. 22.
    Ferm, M. (1979). Method for determination of atmospheric ammonia. Atmospheric Environment, 13(10), 1385–1393.CrossRefGoogle Scholar
  23. 23.
    Ferm, M., De Santis, F., & Varotsos, C. (2005). Nitric acid measurements in connection with corrosion studies. Atmospheric Environment, 39, 6664–6672.CrossRefGoogle Scholar
  24. 24.
    Fraser, M., & Cass, G. (1998). Detection of excess ammonia emissions from in use vehicles and the implications for fine particle control. Environ. Sci. Technol, 32(8), 1053–1057.CrossRefGoogle Scholar
  25. 25.
    Gao, C., Bompard, E., Napoli, R., & Cheng, H. (2007). Price forecast in the competitive electricity market by support vector machine. Physica A: Statistical Mechanics and its Applications, 382(1), 98–113.CrossRefGoogle Scholar
  26. 26.
    Gelle, G., Colas, M., & Delaunay, G. (2000). Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis. Mechanical Systems and Signal Processing, 14(3), 427–442.CrossRefGoogle Scholar
  27. 27.
    Goldberg, D. E. (1989). Genetic algorithm in search, optimization and machine learning. Harlow, England: Addison-Wesley.Google Scholar
  28. 28.
    Gong, L. (2013). Atmospheric ammonia measurements and implications for particulate matter formation in urban and suburban areas of Texas. Ph.D Thesis, Rice University.Google Scholar
  29. 29.
    Gong, L., Lewicki, R., Griffin, R. J., Flynn, J. H., Lefer, B. L., & Tittel, F. K. (2011). Atmospheric ammonia measurements in Houston, TX using an external-cavity quantum cascade laser-based sensor. Atmospheric Chemistry and Physics, 11, 9721–9733.CrossRefGoogle Scholar
  30. 30.
    Grivas, G., & Chaloulakou, A. (2006). Artificial neural network models for predictions of PM10 hourly concentrations in greater area of Athens. Atmospheric Environment, 40(7), 1216–1229.CrossRefGoogle Scholar
  31. 31.
    Hamid, S., & Mirhosseyni, L. (2009). A hybrid fuzzy knowledge-based expert system and genetic algorithm for efficient selection and assignment of material handling equipment. Expert Systems with Applications, 36(9), 11875–11887.CrossRefGoogle Scholar
  32. 32.
    Harren, F. J. M., Cotti, G., Oomens, J., & Lintel Hekkert, S. (2000). Photoacoustic spectroscopy. In R. A. Meyers (Ed.), Trace gas monitoring. Encyclopedia of analytical chemistry (pp. 2203–2226). Chichester: John Wiley & Sons Ltd.Google Scholar
  33. 33.
    Holland, J. H. (1975). Adoption in neural and artificial systems. Ann Arbor, MI, USA: The University of Michigan Press.Google Scholar
  34. 34.
    Hsieh, L. T., & Chen, T. C. (2010). Characteristics of ambient ammonia levels measured in three different industrial parks in southern Taiwan. Aerosol and Air Quality Research, 10, 596–608.Google Scholar
  35. 35.
    Huan, X., Constantine, C., & Shie, M. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.Google Scholar
  36. 36.
    Huang, C. L., & Dun, J. F. (2008). A distributed PSO-SVM hybrid system with feature selection and parameter optimization. Applied Soft Computing, 8(4), 1381–1391.CrossRefGoogle Scholar
  37. 37.
    Kanevski, M., Parkin, R., Pozdnukhov, A., Timonin, V., Maignan, M., Demyanov, V., & Canu, S. (2004). Environmental data mining and modeling based on machine learning algorithms and geostatistics. Environmental Modelling & Software, 19(9), 845–855.CrossRefGoogle Scholar
  38. 38.
    Kawashima, S., & Yonemura, S. (2001). Measuring ammonia concentration over a grassland near livestock facilities using a semiconductor ammonia sensor. Atmospheric Environment, 35(22), 3831–3839.CrossRefGoogle Scholar
  39. 39.
    Kean, A. J., Littlejohn, D., Ban-Weiss, G. A., Harley, R. A., Kirchstetter, T. W., & Lunden, M. M. (2009). Trends in on-road vehicle emissions of ammonia. Atmospheric Environment, 43(8), 1565–1570.CrossRefGoogle Scholar
  40. 40.
    Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In IEEE International Conference on Neural Networks, 4 (pp. 1942–1948).Google Scholar
  41. 41.
    Keuken, M. P., Schoonebeek, C. A. M., Wensveen-Louter, A. V., & Slanina, J. (1988). Simultaneous sampling of NH3, HNO3, HCl, SO2 and H2O2 in ambient air by wet annular denuder system. Atmospheric Environment, 22(11), 2541–2548.CrossRefGoogle Scholar
  42. 42.
    Kim, H. J., & Shin, K. S. (2007). Hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Applied Soft Computing, 7(2), 569–576.CrossRefGoogle Scholar
  43. 43.
    Kolehmainen, M., Martikainen, H., Hiltunen, T., & Ruuskanen, J. (2000). Forecasting air quality parameters using hybrid neural network modeling. Environmental Monitoring and Assessment, 65, 277–286.CrossRefGoogle Scholar
  44. 44.
    Kukkonen, J., Partanen, L., Karpinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S., Chatterton, T., Foxall, R., & Cawley, G. (2003). Extensive evaluation of neural networks models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modeling system and measurements in central Helsinki. Atmospheric Environment, 37(32), 4539–4550.CrossRefGoogle Scholar
  45. 45.
    Lim, Y., Moon, Y. S., & Kim, T. W. (2007). Artificial neural network approach for prediction of ammonia emission from field-applied manure and relative significance assessment of ammonia emission factors. European Journal of Agronomy, 26(4), 425–434.CrossRefGoogle Scholar
  46. 46.
    Lin, J. Y., Cheng, C. T., & Chau, K. W. (2006). Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 51(4), 599–612.CrossRefGoogle Scholar
  47. 47.
    Lin, S. W., Ying, K. C., Chen, S. C., & Lee, Z. J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 35(4), 1817–1824.CrossRefGoogle Scholar
  48. 48.
    Liong, S. Y., & Sivapragasam, C. (2002). Flood stage forecasting with support vector machines. Journal of the American Water Resources Association, 38(1), 173–186.CrossRefGoogle Scholar
  49. 49.
    Liu, C. C., & Chuang, K. W. (2009). An outdoor time scenes simulation scheme based on support vector regression with radial basis function on DCT domain. Image and Vision Computing, 27(10), 1626–1636.CrossRefGoogle Scholar
  50. 50.
    Liu, L. X., Zhuang, Y. Q., & Xue, Y. L. (2011). Tax forecasting theory and model based on SVM optimized by PSO. Expert Systems with Applications, 38(1), 116–120.CrossRefGoogle Scholar
  51. 51.
    Lu, C. J., Lee, T. S., & Chiu, C. C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125.CrossRefGoogle Scholar
  52. 52.
    Lua, W. Z., & Wang, W. J. (2005). Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere, 59(5), 693–701.CrossRefGoogle Scholar
  53. 53.
    Mahdevari, S., Haghighat, H. S., & Torabi, S. R. (2013). A dynamically approach based on SVM algorithm for prediction of tunnel convergence during excavation. Tunnelling and Underground Space Technology, 38, 59–68.CrossRefGoogle Scholar
  54. 54.
    Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software, 15(1), 101–124.CrossRefGoogle Scholar
  55. 55.
    Manne, J., Jager, W., & Tulip, J. (2006). Pulsed quantum cascade laser-based cavity ring-down spectroscopy for ammonia detection in breath. Applied Optics, 45(36), 9230–9237.CrossRefGoogle Scholar
  56. 56.
    Myles, L., Meyers, T. P., & Robinson, L. (2006). Atmospheric NH3 measurement with an ion mobility spectrometer. Atmospheric Environment, 40(30), 5745–5752.CrossRefGoogle Scholar
  57. 57.
    Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). Short-term flood forecasting with a neurofuzzy model. Water Resources Research, 41, W04004. doi:10.1029/2004WR003562.Google Scholar
  58. 58.
    Niska, H., Heikkinen, M., & Kolehmainen, M. (2006). Genetic algorithms and sensitivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series. Lecture Notes in Computer Science, 4224, 224–231.CrossRefGoogle Scholar
  59. 59.
    Niu, D., Li, J., Li, J., & Liu, D. (2009). Middle-long power load forecasting based on particle swarm optimization. Computers & Mathematics with Applications, 57(11–12), 1883–1889.CrossRefGoogle Scholar
  60. 60.
    Noori, R., Khakpour, A., Omidvar, B., & Farokhnia, A. (2010). Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications, 37, 5856–5862.CrossRefGoogle Scholar
  61. 61.
    Nowak, J. B., Huey, L. G., Russell, A. G., Tian, D., Neuman, J. A., Orsini, D., Sjostedt, S. J., Sullivan, A. P., Tanner, D. J., Nenes, A., Edgerton, E., & Fehsenfeld, F. C. (2006). Analysis of urban gas phase ammonia measurements from the 2002 Atlanta Aerosol Nucleation and Real-Time Characterization Experiment (ANARChE). Journal of Geophysical Research: Atmospheres, 111, D17308. doi:10.1029/2006JD007113.CrossRefGoogle Scholar
  62. 62.
    Okkan, U. (2012). Performance of least squares support vector machine for monthly reservoir inflow prediction. Fresenius Environmental Bulletin, 21(3), 611–620.Google Scholar
  63. 63.
    Okkan, U., & Serbes, Z. A. (2012). Rainfall-runoff modeling using least squares support vector machines. Environmetrics, 23, 549–564.CrossRefGoogle Scholar
  64. 64.
    Ordieres, J. B., Vergara, E. P., Capuz, R. S., & Salazar, R. E. (2005). Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juarez (Chihuahua). Environmental Modelling and Software, 20(5), 547–559.CrossRefGoogle Scholar
  65. 65.
    Plöchl, M. (2001). Neural network approach for modelling ammonia emission after manure application on the field. Atmospheric Environment, 35(33), 5833–5841.CrossRefGoogle Scholar
  66. 66.
    Pryor, S. C., Barthelmie, R. J., Sørensen, L. L., & Jensen, B. (2001). Ammonia concentrations and fluxes over a forest in the midwestern USA. Atmospheric Environment, 35(32), 5645–5656.CrossRefGoogle Scholar
  67. 67.
    Rumburg, B., Mount, G. H., Filipy, J., Lamb, B., Westberg, H., Yonge, D., Kincaid, R., & Johnson, K. (2008). Measurement and modeling of atmospheric flux of ammonia from dairy milking cow housing. Atmospheric Environment, 42(14), 3364–3379.CrossRefGoogle Scholar
  68. 68.
    Samui, P. (2008). Slope stability analysis: a support vector machine approach. Environmental Geology, 56, 255–267.CrossRefGoogle Scholar
  69. 69.
    Samui, P. (2011). Application of least square support vector machine (LSSVM) for determination of evaporation losses in reservoirs. Scientific Research, Engineering, 3, 431–434.Google Scholar
  70. 70.
    Samui, P. (2011). Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT. Natural Hazards, 59, 811–822.CrossRefGoogle Scholar
  71. 71.
    Sarwar, G., Corsi, R. L., Kinney, K. A., Banks, J. A., Torres, V. M., & Schmidt, C. (2005). Measurements of ammonia emissions from oak and pine forests and development of a non-industrial ammonia emissions inventory in Texas. Atmospheric Environment, 39(37), 7137–7153.CrossRefGoogle Scholar
  72. 72.
    Simon, P. K., Dasgupta, P. K., & Vecera, Z. (1991). Wet effluent denuder coupled liquid/ion chromatography systems. Analytical Chemistry, 63(13), 1237–1242.CrossRefGoogle Scholar
  73. 73.
    Simon, P. K., & Dasgupta, P. K. (1993). Wet effluent denuder coupled liquid/ion chromatography systems: annular and parallel plate denuders. Analytical Chemistry, 65(9), 1134–1139.CrossRefGoogle Scholar
  74. 74.
    Tripathi, S., Srinivas, V. V., & Nanjundiah, R. S. (2006). Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology, 330, 621–640.CrossRefGoogle Scholar
  75. 75.
    VanderNoot, T. J., & Abrahams, I. (1998). The use of genetic algorithms in the non-linear regression of immittance data. Journal of Electro Analytical Chemistry, 448, 17–23.CrossRefGoogle Scholar
  76. 76.
    Vapnik, V. (1999). The nature of statistical learning theory. New York: Springer–Verlag.Google Scholar
  77. 77.
    Voukantsis, D., Karatzas, K., Kukkonen, J., Räsänen, T., Karppinen, A., & Kolehmainen, M. (2011). Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Science of the Total Environment, 409, 1266–1276.CrossRefGoogle Scholar
  78. 78.
    Walker, J. T., Whitall, D. R., Robarge, W., & Paerl, H. W. (2004). Ambient ammonia and ammonium aerosol across a region of variable ammonia emission density. Atmospheric Environment, 38(9), 1235–1246.CrossRefGoogle Scholar
  79. 79.
    Wang, C. W., Chau, K. W., Cheng, C. T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374, 294–306.CrossRefGoogle Scholar
  80. 80.
    Warneck, P. (1988). Chemistry of the natural atmosphere. New York: Academic Press.Google Scholar
  81. 81.
    Whitehead, J. D., Longley, I. D., & Gallagher, M. W. (2007). Seasonal and diurnal variation in atmospheric ammonia in an urban environment measured using a quantum cascade laser absorption spectrometer. Water, Air, and Soil Pollution, 183, 317–329.CrossRefGoogle Scholar
  82. 82.
    Wieland, R., & Mirschel, W. (2008). Adaptive fuzzy modeling versus artificial neural networks. Environmental Modelling & Software, 23(2), 215–224.CrossRefGoogle Scholar
  83. 83.
    Wieland, R., Mirschel, W., Zbell, B., Groth, K., Pechenick, A., & Fukuda, K. (2010). A new library to combine artificial neural networks and support vector machines with statistics and a database engine for application in environmental modeling. Environmental Modelling & Software, 25(4), 412–420.CrossRefGoogle Scholar
  84. 84.
    Wilson, S. M., & Serre, M. L. (2007). Use of passive samplers to measure atmospheric ammonia levels in a high-density industrial hog farm area of eastern North Carolina. Atmospheric Environment, 41(28), 6074–6086.CrossRefGoogle Scholar
  85. 85.
    Wyers, G. P., Otjes, R. P., & Slanina, J. (1993). A continuous flow denuder for the measurement of ambient concentrations and surface fluxes of NH3. Atmospheric Environment, 27(13), 2085–2090.CrossRefGoogle Scholar
  86. 86.
    Xanthopoulos, P., Pardalos, P. M., & Trafalis, T. B. (2013). Robust data mining. Springer.Google Scholar
  87. 87.
    Yokelson, R. J., Bertschi, I. T., Christian, T. J., Hobbs, P. V., Ward, D. E., & Hao, W. M. (2002). Trace gas measurements in nascent, aged, and cloud processed smoke from African savanna fires by airborne Fourier transform infrared spectroscopy (AFTIR). Geophysical Research: Atmospheres, 108(D13), 8472. doi:10.1029/2002JD002100.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jiawei Zhang
    • 1
    • 2
  • Frank K. Tittel
    • 2
  • Longwen Gong
    • 3
  • Rafal Lewicki
    • 4
  • Robert J. Griffin
    • 3
  • Wenzhe Jiang
    • 2
  • Bin Jiang
    • 5
  • Mingbao Li
    • 6
  1. 1.College of Mechanical and Electrical EngineeringNortheast Forestry UniversityHarbinChina
  2. 2.Department of Electrical and Computer EngineeringRice UniversityHoustonUSA
  3. 3.Department of Civil and Environmental EngineeringRice UniversityHoustonUSA
  4. 4.Sentinel Photonics CompanyPlainsboroUSA
  5. 5.Harbin Research Institute of Electrical InstrumentHarbinChina
  6. 6.College of Civil EngineeringNortheast Forestry UniversityHarbinChina

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