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An algorithm and a device for counting airborne pollen automatically using laser optics
ARTICLE IN PRESS
Atmospheric Environment 41 (2007) 7987–7993 www.elsevier.com/locate/atmosenv
Technical note
An algorithm and a device for counting airborne pollen automatically using laser optics Shigeto Kawashimaa,, Bernard Clotb, Toshio Fujitac, Yuichi Takahashid, Kimihito Nakamuraa a
Kyoto University, Kitashirakawa, Kyoto 606-8502, Japan MeteoSwiss, Station ae´rologique, CH– 1530 Payerne, Switzerland c Yamato Engineering Company Ltd., Heiseicho, Yokosuka 238-0013, Japan d Yamagata Prefectural Institute of Public Health, Tohkamachi, Yamagata 990-0031, Japan b
Received 29 March 2007; received in revised form 2 September 2007; accepted 12 September 2007
Abstract Airborne pollen is important in relation to the social issues of pollinosis and of the environmental effects of genetically modified plants. Existing methods for pollen counting involve counting and classifying the grains that adhere to a sampling surface, requiring much time and skilled labor. We therefore have developed a method of automatically monitoring pollen, using a laser-optics instrument. In this instrument, the sideways and forward scattering of laser light by each particle is recorded in real time for computer processing. A field experiment was conducted in 2005, comparing our method with that of the older Hirst method. A scatter plot was made of the forward scattering vs. the sideways scattering for each particle. An algorithm was developed to find the optimum rectangular region of the plot for each type of pollen, and a count of points inside this region was taken as the count for that type of pollen. For the three most common types of pollen found in the field test (Urticaceae, Poaceae, and Ambrosia), the daily counts from this algorithm were compared with the daily counts from the Hirst-type (Burkard) sampler. There was a very high correlation (determination coefficient approximately 0.8) between the results of the two methods. r 2007 Elsevier Ltd. All rights reserved. Keywords: Airborne pollen; Concentration; Automatic pollen monitoring; Semiconductor laser
1. Introduction Airborne pollen is important in relation to two social issues today. One is the health problem of pollinosis, and the other is the environmental effects of genetically modified crops. Corresponding author. Tel.: +81 75 753 6154; fax: +81 75 753 6476. E-mail address: [email protected] (S. Kawashima).
1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.09.019
In recent years, the number of pollinosis cases has increased rapidly, and the disease is becoming a serious social problem. In Japan, much aerobiological research related to cedar pollinosis has been undertaken to clarify the spatial and temporal variations of airborne-pollen concentration (e.g., Kawashima and Takahashi, 1995, 1999). To provide the pollen information promptly and adequately, an automatic real-time pollen-measurement method is needed.
ARTICLE IN PRESS 7988
S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
The safety and the impact on the environment of transgenic crops are also important issues. Losey et al. (1999) first suggested that pollen from transgenic Bt (Bacillus thuringiensis) corn (Zea mays L.) might kill non-target insects, a potentially significant problem. There is also a gene-flow problem of dispersal of artificially-modified genes to the natural environment by airborne pollen (Gliddon, 1999; Paul et al., 1995; Kwon et al., 2001). Especially for wind-pollinated crops, there is a possibility that pollen diffuses quite widely, depending on meteorological conditions (Quist and Chapela, 2001; Louette et al., 1997; Kawashima et al., 2002, 2005). To deal appropriately with the environmental problems of genetically-modified crops, it is necessary that we clarify the dispersal movement of airborne pollen in more detail. Just as for the pollinosis problem, an easy and prompt measurement method for airborne pollen is needed. The existing measurement methods for airborne pollen can be divided roughly into gravitational methods and volumetric methods. The Durham method (Durham, 1946a, b), a widely used gravitational method, has the merit of using a simple and low cost device; the Burkard method (Hirst, 1952), a widely used volumetric method, has the merit that it can measure the hourly variation in airborne pollen concentration. In the existing methods, it is necessary to count the number of pollen grains that adhere to a sampling material. The counting work, using a microscope, requires much labor and time. Skill and concentration are necessary for distinguishing and counting of the target pollen. Counting errors occur, depending on the personal skill level. In recent years, several investigations were performed into technologies for measuring airborne pollen automatically (e.g., Bennett, 1990; Bechar et al., 1997; Aronne et al., 2001). The aim of these was to develop automatic recognition methods for pollen on sampled materials, by using imageanalysis technology. Automatic counting by image analysis, like the existing methods, relies on the adhesion of pollen onto a sampling surface. Therefore, real-time acquisition of measured values is difficult. A particle counter was used in another attempt at automated measurement of pollen (Harder, 1990; Young and Stanton, 1990). Because this approach was a simple application of a particle counter, it only counted the number of particles within a given size range. It was therefore difficult to distinguish pollen grains from dust of similar size.
The advancement of laser-optics technology in recent years has enabled its application to various fields. We have aimed to develop an automatic monitoring device for airborne pollen using this technology. We propose a new methodology based on the laser particle counter to measure the optical properties and the hydrodynamic characteristics of pollen grains. In this paper, we show an algorithm for counting several kinds of airborne pollen grains at the same time. Its effectiveness is proven by the data observed in a natural outdoor setting. 2. Materials and methods 2.1. Experimental site and pollen measurement A field experiment was performed during August and September of 2005, in Neuchatel, Switzerland. The altitude of the city is about 490 m. It is located on the shore of Neuchatel Lake. A Hirst-type pollen sampler (Burkard Manufacturing Co., Ltd.) and our automatic monitoring device for airborne pollen were set up next to each other on the roof of a building of Neuchatel University, near the center of the city. An overall diagram of the measurement system is shown in Fig. 1. (Our monitoring device is called the ‘‘automatic pollen monitor’’ hereinafter.) Meteorological data were measured at the same time. For this report, we analyzed the data from 30 August to 8 September. Good weather continued during the period, and relatively many kinds of airborne pollen were observed. The Burkard sampler used a 7-day sampler element. We obtained pollen concentration data in grains m 3. We summarized the count data as the daily average concentration. 2.2. Outline of the automatic pollen monitor Fig. 2a shows an outline of the airflow system of the automatic pollen monitor. A conical glass funnel (65 mm maximum diameter) is installed in the air intake of the monitor. The open direction of the funnel is directed upward in order to avoid the effect of wind direction. The lower part of the air intake is connected to the dust-filtering device. High-density particles fall downward quickly and are removed. The air that contains the remaining particles, mostly pollen grains, is introduced into the optical system, and is irradiated by the semiconductor laser beam. When a particle such as a pollen
ARTICLE IN PRESS S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
Rain guard
Hirst type pollen sampler
Intake funnel
Standard Method
7989
Air tube Dust Buffer
Side by side
Pollen monitor
Personal Computer RS-232C
AC Power
Fig. 1. Outline of the measurement system. The Burkard (Hirst-type) pollen sampler and the automatic pollen monitor were set up next to each other on the roof of a building.
Air inlet Air outlet
Flow meter
Large dust filtering device
Valve
Filter Pump
Optical system
Filter
Buffer tank
Air Air tube
PIN-PD detector Collimator lens
Air tube
Laser diode Cyclic air
Cylindrical lens PIN-PD detector Fig. 2. Mechanical diagrams of the automatic pollen monitor. (a) Airflow system, (b) optical system.
ARTICLE IN PRESS S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
grain passes through the thin, wide laser beam, it is detected by its scattering of the laser light. After the detection section, the air passes through a filter, which removes the particle, and then is sucked into the pump through the buffer tank, where the pulsation of the pump is reduced. A part of the exhaust air from the pump returns to the optical system through another filter. This current of air wraps around the sucked column of air in the optical system that contains pollen. This method not only prevents dirt in the optical system but also improves the response of the detector to changes in the density of pollen; we call it the ‘‘air jacket’’ method. The intake airflow was set to 4.1 L min 1, in consideration of the capacity of the pump and the characteristics of the optical system. This airflow is half that of the Burkard sampler. The most important part of the automatic pollen monitor is the optical system; Fig. 2b shows an outline of this system. A semiconductor laser of wavelength 780 nm, output 3 mW, sends its output through a cylindrical lens and a collimating lens, giving a beam through the sensing region in the shape of a wide horizontal ribbon. The ribbon beam is about 30 mm thick. The scattered light from a particle in the ribbon beam is detected by two detecting elements (PIN-PD), one for forward scattering and the other for sideways scattering. The forward- and sideways-scattered pulses induced by each particle are measured and digitized for transfer to a personal computer via an RS-232 connection. The peak-voltage data and the occurrence time are recorded in the computer. 3. Results 3.1. Data obtained by the Burkard sampler The maximum number of pollen grains, 110, was obtained for the Urticaceae pollen. The second largest number of grains, 67, was observed for Poaceae pollen. The third largest number of grains, 22, was observed for Ambrosia pollen. For the other kinds of pollen, 10 grains or more were observed for Chenopodium, Mercurialis, and Plantago. The data obtained by the Burkard sampler was arranged as daily values (Fig. 3) in order to see the temporal variation. Many Urticaceae grains were observed at the start of the monitoring period, but the number decreased rapidly in the latter half of the period. A fairly large number of Poaceae grains was observed in the first half of the monitoring period, and these
Number of airborne pollen (grains / day)
7990
35 30 25
Ambrosia Poaceae Urticaceae
20 15 10 5 0 8/30 8/31 9/1 9/2 9/3 9/4 9/5 9/6 9/7 9/8 9/9
Fig. 3. Temporal variation in the number of major kinds of airborne pollen grains obtained by the Burkard sampler.
also decreased in the latter half of the period. Ambrosia pollen peaked on 1 and 5 September. All types of airborne pollen decreased through the monitoring period. In this paper, the most frequent three kinds of pollen, Urticaceae, Poaceae, and Ambrosia, are treated as the targets for further analysis. 3.2. Data obtained by the automatic pollen monitor For the automatic pollen monitor, the relation between the intensities of side and forward scattering was not linear (Fig. 4). If the relation between the scatterings in the two directions were linear, there would be no advantage to measuring these data simultaneously. As the relation is not linear, there is a possibility that each scattering point provides individual information. The intensity of light scattered by particles varies with particle size, shape, color, surface roughness, reflectance, etc. When the particles are pollen grains, the factor with the largest influence is grain size. The intensity of scattered light varies with scattering angle. We measured the intensities of scattered light in two representative directions, sideways and forward. The influence of each factor is different for sideways scattering and forward scattering. The mean intensity of the scattered light is, however, mainly related to particle size and reflectance. The difference between sideways and forward intensities of the scattered light is mainly related to particle shape and surface roughness (Xu, 2000; Mishchenko et al., 2000). The expected ranges for the two directions of scattering define a rectangular area inside the scatter chart (Fig. 4) of sideways scattering intensity vs. forward scattering intensity. The points in the scatter chart are extracted through
ARTICLE IN PRESS S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
4500
7991
40 Poaceae
4000
35 length of minor axis (µm)
Forward scattering (mV)
3500 3000 2500 2000 1500
30 25 Ambrosia
20
1000
15 500 0
500 1000 1500 2000 2500 3000 3500 4000 4500 Sideward scattering (mV)
Fig. 4. Scatter plot, forward scattering vs. sideways scattering, of the particles observed by the automatic pollen monitor during the monitoring period.
the window of this rectangular area. Therefore, we call this rectangular area the ‘‘extract window’’. The sizes of three kinds of target pollen grains were measured in order to decide the rough firstapproximation positions of the extract window for each type of target pollen. The sizes were measured with a microscope for pollen obtained by the Burkard sampler. For each type of pollen, we plotted the length of the minor axis of the grain vs. the length of its major axis, and the standard deviation of the data (Fig. 5). The relative intensity of the scattered light for the three kinds of target pollen was first assumed from this figure, and firstapproximation extract windows were decided. For Urticaceae, the extract window is toward the left of Fig. 4 scatter chart. For Poaceae it is at the upper right, and for Ambrosia it is between the window for Urticaceae and the window for Poaceae. These locations are the initial assumed locations, before the locations and sizes of the extract windows were improved by comparing the monitor pollen counts with the observed counts from the Burkard sampler. 3.3. Airborne pollen estimated from the automatic pollen monitor The Urticaceae pollen is representative of the small-size pollen observed in this study: the mean particle size is about 13 mm. The final extract window for the Urticaceae pollen is located toward the left of the scatter chart (Fig. 6). The counts of
10
15
20
25
30
35
40
length of major axis (µm) Fig. 5. Measured lengths and breadths of the three major kinds of target pollen grains. Standard deviations are shown by the error bars.
4500 4000 Forward scattering (mV)
0
Urticaceae 10
3500 3000 2500 2000 urticaceae
1500 1000
ambrosia
500
poaceae
0 0
500 1000 1500 2000 2500 3000 3500 4000 4500 Sideward scattering (mV)
Fig. 6. Extract windows for three kinds of target pollens in the scatter plot (forward vs. sideways scattering).
the automatic pollen monitor are very highly correlated with the counts of the Burkard sampler; the coefficient of determination (r2) is 0.83. For Poaceae pollen, which is representative of the largesize pollen observed in this study; the mean particle size is about 45 mm. The final Poaceae pollen extract window is located toward the upper right of the scatter chart of side vs. forward scattering intensity (Fig. 6). r2 is 0.81, again showing a high correlation for the two methods. Ambrosia pollen is the representative of medium-size pollen observed in this study: the mean particle size is about 18 mm. The
ARTICLE IN PRESS S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
7992
final Ambrosia pollen extract window is located to the left of the middle of the scatter chart of side vs. forward scattering intensity (Fig. 6). r2 is 0.78, again showing a high correlation for the two methods. 3.4. Comparison of two methods in temporal variation Both methods show, for Urticaceae pollen, a large number of counts at the beginning, and a rapidly decreasing number later (Fig. 7). Two long Poaceae pollen peaks and two Ambrosia pollen peaks were found by both methods. These figures show that the automatic pollen monitor can adequately estimate the temporal variation in these three kinds of pollen.
Number of pollen (grains/day)
35
Urticaceae Laser Count
30
Burkard
25 20 15 10 5 0
8/30 8/31
9/1
9/2
9/3
9/4
9/5
9/6
9/7
9/8
Number of pollen (grains/day)
16 14
Poaceae
Laser Count Burkard
12 10 8 6 4 2 0 8/30 8/31
9/1
9/2
9/3
9/4
9/5
9/6
9/7
9/8
Number of pollen (grains/day)
9 8
Ambrosia
Laser Count Burkard
7 6 5
4. Discussion We aimed to develop an automatic pollen monitor that could be used for counting several kinds of airborne pollen simultaneously and automatically in real time. As a first step of the research, we have developed a laser pulse monitor that can output raw signals of scattering light intensity. A field experiment was conducted at Neuchatel in Switzerland in 2005. We examined the appropriate ranges of the sidescattering intensity and those of the forward-scattering intensity. The area limited by these ranges in the scattergram is called an ‘‘extract window.’’ The counts obtained by the laser pulse monitor using the extract windows showed high correlation with the daily data obtained by the Burkard sampler. For the laser pulse monitor, although the basic strategy was proven, a number of problems remain. Any particle that has sideways and forward scattering similar to those of the target pollen is misrecognized as target pollen. Two different kinds of pollen with similar scattering characteristics cannot be distinguished. In this paper, we show a new basic algorithm to measure concentrations of several kinds of airborne pollen simultaneously, using the intensities of two directions of scattering of semiconductor laser light. Although analytical clarification of the optical process may be not complete enough for people who work in pollen morphology and pollen discrimination, our method will give new and valuable information to those who wish to obtain approximate real-time concentrations or detailed spatial distributions of problematic airborne pollen types. For further study, we will obtain a number of data sets during various seasons, and clarify the ranges of the extract windows for various kinds of pollen. Then, we aim to generalize the algorithm in this paper to realtime automatic pollen monitoring technology. Furthermore, we are planning to develop a doublebeam method that uses laser beams of two wavelengths. All this technology and know-how is contributed for an epoch-making method to measure airborne pollen automatically in real time.
4
Acknowledgments
3 2 1 0
8/30 8/31
9/1
9/2
9/3
9/4
9/5
9/6
9/7
9/8
Fig. 7. Daily variation of the airborne pollen counts for the three kinds of pollen, as observed by the two methods.
We thank Dr. Kenjiro Oda of the National Institute for Agro-Environmental Sciences for greatly inspiring discussions. We also thank Ms. Shizuko Suzuki and Ms. Ikuko Utagawa of the secretary section at the Institute for performing many tasks on our behalf.
ARTICLE IN PRESS S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
References Aronne, G., Cavuoto, D., Eduardo, P., 2001. Classification and counting of fluorescent pollen using an image analysis system. Biotechnic and Histochemistry 76, 35–40. Bechar, A., Gan-Mor, S., Vaknin, Y., Shemulevich, I., Ronen, B., Eisikowitch, D., 1997. An image-analysis technique for accurate counting of pollen on stigmas. New Phytology 137, 639–643. Bennett, K.D., 1990. Pollen counting on a pocket computer. New Phytology 114, 275–280. Durham, O.C., 1946a. The volumetric incidence of atmospheric allergens. III. Rate of fall of pollen grains in still air. Journal of Allergy 17, 70–78. Durham, O.C., 1946b. The volumetric incidence of atmospheric allergens. IV. A proposed standard method of gravity sampling, counting, and volumetric interpolation of results. Journal of Allergy 17, 79–86. Gliddon, C.J., 1999. Gene flow and risk assessment. In: Lutman, P.J.W. (Ed.), Gene Flow and Agriculture. Relevance for Transgenic Crops. University of Keele. British Crop Protection Council, Farnham, Surrey, UK, pp. 49–56. Harder, L.D., 1990. Pollen removal by bumble bees and its implications for pollen dispersal. Ecology 71, 1110–1125. Hirst, J.M., 1952. An automatic volumetric spore trap. Annals of Applied Biology 39, 257–265. Kawashima, S., Takahashi, Y., 1995. Modelling and simulation of mesoscale dispersion processes for airborne cedar pollen. Grana 34, 142–150. Kawashima, S., Takahashi, Y., 1999. An improved simulation of mesoscale dispersion of airborne cedar pollen using a flowering-time map. Grana 38, 316–324.
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Kawashima, S., Matsuo, K., Du, M., Oka, M., Daido, H., Takahashi, Y., Kobayashi, T., Inoue, S., Yonemura, S., 2002. Relationship between percentage of wind-pollinated maize hybrids and distance from donor pollen source. Japan Journal of Palynology 48, 1–12. Kawashima, S., Matsuo, K., Du, M., Takahashi, Y., Inoue, S., Yonemura, S., 2005. An algorithm for estimating potential deposition of corn pollen for environmental assessment. Environmental Biosafety Research 3, 197–207. Kwon, Y.W., Kim, D.S., Yim, K.-O., 2001. Herbicide-resistant genetically-modified crop: assessment and management of gene flow. Weed Biology and Management 1, 96–107. Losey, J.E., Raynor, L.S., Carter, M.E., 1999. Transgenic pollen harms monarch larvae. Nature 399, 214. Louette, D., Charrier, A., Berthand, J., 1997. In situ conservation of maize in Mexico: genetic diversity and maize-seed management in a traditional community. Economic Botany 51, 20–38. Mishchenko, M.I., Hovenier, J.W., Travis, L.D., 2000. Light Scattering by Nonspherical Particles, Theory, Measurements, and Applications. Academic Press, San Diego, USA. Paul, E.M., Thompson, C., Dunwell, J.M., 1995. Gene dispersal from genetically modified oilseed rape in the field. Euphytica 81, 283–289. Quist, D., Chapela, I.H., 2001. Transgenic DNA introgressed into traditional maize landraces in Oaxaca, Mexico. Nature 414, 541–543. Xu, R., 2000. Particle Characterization: Light-Scattering Methods. Kluwer Academic Publishers, Dordrecht, The Netherlands. Young, H.J., Stanton, M.L., 1990. Influences of floral variation on pollen removal and seed production in wild radish. Ecology 71, 536–547.
Atmospheric Environment 41 (2007) 7987–7993 www.elsevier.com/locate/atmosenv
Technical note
An algorithm and a device for counting airborne pollen automatically using laser optics Shigeto Kawashimaa,, Bernard Clotb, Toshio Fujitac, Yuichi Takahashid, Kimihito Nakamuraa a
Kyoto University, Kitashirakawa, Kyoto 606-8502, Japan MeteoSwiss, Station ae´rologique, CH– 1530 Payerne, Switzerland c Yamato Engineering Company Ltd., Heiseicho, Yokosuka 238-0013, Japan d Yamagata Prefectural Institute of Public Health, Tohkamachi, Yamagata 990-0031, Japan b
Received 29 March 2007; received in revised form 2 September 2007; accepted 12 September 2007
Abstract Airborne pollen is important in relation to the social issues of pollinosis and of the environmental effects of genetically modified plants. Existing methods for pollen counting involve counting and classifying the grains that adhere to a sampling surface, requiring much time and skilled labor. We therefore have developed a method of automatically monitoring pollen, using a laser-optics instrument. In this instrument, the sideways and forward scattering of laser light by each particle is recorded in real time for computer processing. A field experiment was conducted in 2005, comparing our method with that of the older Hirst method. A scatter plot was made of the forward scattering vs. the sideways scattering for each particle. An algorithm was developed to find the optimum rectangular region of the plot for each type of pollen, and a count of points inside this region was taken as the count for that type of pollen. For the three most common types of pollen found in the field test (Urticaceae, Poaceae, and Ambrosia), the daily counts from this algorithm were compared with the daily counts from the Hirst-type (Burkard) sampler. There was a very high correlation (determination coefficient approximately 0.8) between the results of the two methods. r 2007 Elsevier Ltd. All rights reserved. Keywords: Airborne pollen; Concentration; Automatic pollen monitoring; Semiconductor laser
1. Introduction Airborne pollen is important in relation to two social issues today. One is the health problem of pollinosis, and the other is the environmental effects of genetically modified crops. Corresponding author. Tel.: +81 75 753 6154; fax: +81 75 753 6476. E-mail address: [email protected] (S. Kawashima).
1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.09.019
In recent years, the number of pollinosis cases has increased rapidly, and the disease is becoming a serious social problem. In Japan, much aerobiological research related to cedar pollinosis has been undertaken to clarify the spatial and temporal variations of airborne-pollen concentration (e.g., Kawashima and Takahashi, 1995, 1999). To provide the pollen information promptly and adequately, an automatic real-time pollen-measurement method is needed.
ARTICLE IN PRESS 7988
S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
The safety and the impact on the environment of transgenic crops are also important issues. Losey et al. (1999) first suggested that pollen from transgenic Bt (Bacillus thuringiensis) corn (Zea mays L.) might kill non-target insects, a potentially significant problem. There is also a gene-flow problem of dispersal of artificially-modified genes to the natural environment by airborne pollen (Gliddon, 1999; Paul et al., 1995; Kwon et al., 2001). Especially for wind-pollinated crops, there is a possibility that pollen diffuses quite widely, depending on meteorological conditions (Quist and Chapela, 2001; Louette et al., 1997; Kawashima et al., 2002, 2005). To deal appropriately with the environmental problems of genetically-modified crops, it is necessary that we clarify the dispersal movement of airborne pollen in more detail. Just as for the pollinosis problem, an easy and prompt measurement method for airborne pollen is needed. The existing measurement methods for airborne pollen can be divided roughly into gravitational methods and volumetric methods. The Durham method (Durham, 1946a, b), a widely used gravitational method, has the merit of using a simple and low cost device; the Burkard method (Hirst, 1952), a widely used volumetric method, has the merit that it can measure the hourly variation in airborne pollen concentration. In the existing methods, it is necessary to count the number of pollen grains that adhere to a sampling material. The counting work, using a microscope, requires much labor and time. Skill and concentration are necessary for distinguishing and counting of the target pollen. Counting errors occur, depending on the personal skill level. In recent years, several investigations were performed into technologies for measuring airborne pollen automatically (e.g., Bennett, 1990; Bechar et al., 1997; Aronne et al., 2001). The aim of these was to develop automatic recognition methods for pollen on sampled materials, by using imageanalysis technology. Automatic counting by image analysis, like the existing methods, relies on the adhesion of pollen onto a sampling surface. Therefore, real-time acquisition of measured values is difficult. A particle counter was used in another attempt at automated measurement of pollen (Harder, 1990; Young and Stanton, 1990). Because this approach was a simple application of a particle counter, it only counted the number of particles within a given size range. It was therefore difficult to distinguish pollen grains from dust of similar size.
The advancement of laser-optics technology in recent years has enabled its application to various fields. We have aimed to develop an automatic monitoring device for airborne pollen using this technology. We propose a new methodology based on the laser particle counter to measure the optical properties and the hydrodynamic characteristics of pollen grains. In this paper, we show an algorithm for counting several kinds of airborne pollen grains at the same time. Its effectiveness is proven by the data observed in a natural outdoor setting. 2. Materials and methods 2.1. Experimental site and pollen measurement A field experiment was performed during August and September of 2005, in Neuchatel, Switzerland. The altitude of the city is about 490 m. It is located on the shore of Neuchatel Lake. A Hirst-type pollen sampler (Burkard Manufacturing Co., Ltd.) and our automatic monitoring device for airborne pollen were set up next to each other on the roof of a building of Neuchatel University, near the center of the city. An overall diagram of the measurement system is shown in Fig. 1. (Our monitoring device is called the ‘‘automatic pollen monitor’’ hereinafter.) Meteorological data were measured at the same time. For this report, we analyzed the data from 30 August to 8 September. Good weather continued during the period, and relatively many kinds of airborne pollen were observed. The Burkard sampler used a 7-day sampler element. We obtained pollen concentration data in grains m 3. We summarized the count data as the daily average concentration. 2.2. Outline of the automatic pollen monitor Fig. 2a shows an outline of the airflow system of the automatic pollen monitor. A conical glass funnel (65 mm maximum diameter) is installed in the air intake of the monitor. The open direction of the funnel is directed upward in order to avoid the effect of wind direction. The lower part of the air intake is connected to the dust-filtering device. High-density particles fall downward quickly and are removed. The air that contains the remaining particles, mostly pollen grains, is introduced into the optical system, and is irradiated by the semiconductor laser beam. When a particle such as a pollen
ARTICLE IN PRESS S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
Rain guard
Hirst type pollen sampler
Intake funnel
Standard Method
7989
Air tube Dust Buffer
Side by side
Pollen monitor
Personal Computer RS-232C
AC Power
Fig. 1. Outline of the measurement system. The Burkard (Hirst-type) pollen sampler and the automatic pollen monitor were set up next to each other on the roof of a building.
Air inlet Air outlet
Flow meter
Large dust filtering device
Valve
Filter Pump
Optical system
Filter
Buffer tank
Air Air tube
PIN-PD detector Collimator lens
Air tube
Laser diode Cyclic air
Cylindrical lens PIN-PD detector Fig. 2. Mechanical diagrams of the automatic pollen monitor. (a) Airflow system, (b) optical system.
ARTICLE IN PRESS S. Kawashima et al. / Atmospheric Environment 41 (2007) 7987–7993
grain passes through the thin, wide laser beam, it is detected by its scattering of the laser light. After the detection section, the air passes through a filter, which removes the particle, and then is sucked into the pump through the buffer tank, where the pulsation of the pump is reduced. A part of the exhaust air from the pump returns to the optical system through another filter. This current of air wraps around the sucked column of air in the optical system that contains pollen. This method not only prevents dirt in the optical system but also improves the response of the detector to changes in the density of pollen; we call it the ‘‘air jacket’’ method. The intake airflow was set to 4.1 L min 1, in consideration of the capacity of the pump and the characteristics of the optical system. This airflow is half that of the Burkard sampler. The most important part of the automatic pollen monitor is the optical system; Fig. 2b shows an outline of this system. A semiconductor laser of wavelength 780 nm, output 3 mW, sends its output through a cylindrical lens and a collimating lens, giving a beam through the sensing region in the shape of a wide horizontal ribbon. The ribbon beam is about 30 mm thick. The scattered light from a particle in the ribbon beam is detected by two detecting elements (PIN-PD), one for forward scattering and the other for sideways scattering. The forward- and sideways-scattered pulses induced by each particle are measured and digitized for transfer to a personal computer via an RS-232 connection. The peak-voltage data and the occurrence time are recorded in the computer. 3. Results 3.1. Data obtained by the Burkard sampler The maximum number of pollen grains, 110, was obtained for the Urticaceae pollen. The second largest number of grains, 67, was observed for Poaceae pollen. The third largest number of grains, 22, was observed for Ambrosia pollen. For the other kinds of pollen, 10 grains or more were observed for Chenopodium, Mercurialis, and Plantago. The data obtained by the Burkard sampler was arranged as daily values (Fig. 3) in order to see the temporal variation. Many Urticaceae grains were observed at the start of the monitoring period, but the number decreased rapidly in the latter half of the period. A fairly large number of Poaceae grains was observed in the first half of the monitoring period, and these
Number of airborne pollen (grains / day)
7990
35 30 25
Ambrosia Poaceae Urticaceae
20 15 10 5 0 8/30 8/31 9/1 9/2 9/3 9/4 9/5 9/6 9/7 9/8 9/9
Fig. 3. Temporal variation in the number of major kinds of airborne pollen grains obtained by the Burkard sampler.
also decreased in the latter half of the period. Ambrosia pollen peaked on 1 and 5 September. All types of airborne pollen decreased through the monitoring period. In this paper, the most frequent three kinds of pollen, Urticaceae, Poaceae, and Ambrosia, are treated as the targets for further analysis. 3.2. Data obtained by the automatic pollen monitor For the automatic pollen monitor, the relation between the intensities of side and forward scattering was not linear (Fig. 4). If the relation between the scatterings in the two directions were linear, there would be no advantage to measuring these data simultaneously. As the relation is not linear, there is a possibility that each scattering point provides individual information. The intensity of light scattered by particles varies with particle size, shape, color, surface roughness, reflectance, etc. When the particles are pollen grains, the factor with the largest influence is grain size. The intensity of scattered light varies with scattering angle. We measured the intensities of scattered light in two representative directions, sideways and forward. The influence of each factor is different for sideways scattering and forward scattering. The mean intensity of the scattered light is, however, mainly related to particle size and reflectance. The difference between sideways and forward intensities of the scattered light is mainly related to particle shape and surface roughness (Xu, 2000; Mishchenko et al., 2000). The expected ranges for the two directions of scattering define a rectangular area inside the scatter chart (Fig. 4) of sideways scattering intensity vs. forward scattering intensity. The points in the scatter chart are extracted through
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Fig. 4. Scatter plot, forward scattering vs. sideways scattering, of the particles observed by the automatic pollen monitor during the monitoring period.
the window of this rectangular area. Therefore, we call this rectangular area the ‘‘extract window’’. The sizes of three kinds of target pollen grains were measured in order to decide the rough firstapproximation positions of the extract window for each type of target pollen. The sizes were measured with a microscope for pollen obtained by the Burkard sampler. For each type of pollen, we plotted the length of the minor axis of the grain vs. the length of its major axis, and the standard deviation of the data (Fig. 5). The relative intensity of the scattered light for the three kinds of target pollen was first assumed from this figure, and firstapproximation extract windows were decided. For Urticaceae, the extract window is toward the left of Fig. 4 scatter chart. For Poaceae it is at the upper right, and for Ambrosia it is between the window for Urticaceae and the window for Poaceae. These locations are the initial assumed locations, before the locations and sizes of the extract windows were improved by comparing the monitor pollen counts with the observed counts from the Burkard sampler. 3.3. Airborne pollen estimated from the automatic pollen monitor The Urticaceae pollen is representative of the small-size pollen observed in this study: the mean particle size is about 13 mm. The final extract window for the Urticaceae pollen is located toward the left of the scatter chart (Fig. 6). The counts of
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length of major axis (µm) Fig. 5. Measured lengths and breadths of the three major kinds of target pollen grains. Standard deviations are shown by the error bars.
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Fig. 6. Extract windows for three kinds of target pollens in the scatter plot (forward vs. sideways scattering).
the automatic pollen monitor are very highly correlated with the counts of the Burkard sampler; the coefficient of determination (r2) is 0.83. For Poaceae pollen, which is representative of the largesize pollen observed in this study; the mean particle size is about 45 mm. The final Poaceae pollen extract window is located toward the upper right of the scatter chart of side vs. forward scattering intensity (Fig. 6). r2 is 0.81, again showing a high correlation for the two methods. Ambrosia pollen is the representative of medium-size pollen observed in this study: the mean particle size is about 18 mm. The
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final Ambrosia pollen extract window is located to the left of the middle of the scatter chart of side vs. forward scattering intensity (Fig. 6). r2 is 0.78, again showing a high correlation for the two methods. 3.4. Comparison of two methods in temporal variation Both methods show, for Urticaceae pollen, a large number of counts at the beginning, and a rapidly decreasing number later (Fig. 7). Two long Poaceae pollen peaks and two Ambrosia pollen peaks were found by both methods. These figures show that the automatic pollen monitor can adequately estimate the temporal variation in these three kinds of pollen.
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4. Discussion We aimed to develop an automatic pollen monitor that could be used for counting several kinds of airborne pollen simultaneously and automatically in real time. As a first step of the research, we have developed a laser pulse monitor that can output raw signals of scattering light intensity. A field experiment was conducted at Neuchatel in Switzerland in 2005. We examined the appropriate ranges of the sidescattering intensity and those of the forward-scattering intensity. The area limited by these ranges in the scattergram is called an ‘‘extract window.’’ The counts obtained by the laser pulse monitor using the extract windows showed high correlation with the daily data obtained by the Burkard sampler. For the laser pulse monitor, although the basic strategy was proven, a number of problems remain. Any particle that has sideways and forward scattering similar to those of the target pollen is misrecognized as target pollen. Two different kinds of pollen with similar scattering characteristics cannot be distinguished. In this paper, we show a new basic algorithm to measure concentrations of several kinds of airborne pollen simultaneously, using the intensities of two directions of scattering of semiconductor laser light. Although analytical clarification of the optical process may be not complete enough for people who work in pollen morphology and pollen discrimination, our method will give new and valuable information to those who wish to obtain approximate real-time concentrations or detailed spatial distributions of problematic airborne pollen types. For further study, we will obtain a number of data sets during various seasons, and clarify the ranges of the extract windows for various kinds of pollen. Then, we aim to generalize the algorithm in this paper to realtime automatic pollen monitoring technology. Furthermore, we are planning to develop a doublebeam method that uses laser beams of two wavelengths. All this technology and know-how is contributed for an epoch-making method to measure airborne pollen automatically in real time.
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Fig. 7. Daily variation of the airborne pollen counts for the three kinds of pollen, as observed by the two methods.
We thank Dr. Kenjiro Oda of the National Institute for Agro-Environmental Sciences for greatly inspiring discussions. We also thank Ms. Shizuko Suzuki and Ms. Ikuko Utagawa of the secretary section at the Institute for performing many tasks on our behalf.
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