Abstract This paper reports12 that a digital single-lens reflex (SLR) camera can be applied as a remote sensor for monitoring the concentrations of particulate ...
Development of Air Quality Monitoring Remote Sensor Using a Digital SLR Camera C.J. Wong, M. Z. MatJafri, K. Abdullah, H. S. Lim and K. L Low School of Physics, Universiti Sains Malaysia 11800 USM, Penang, Malaysia. Tel: +604-6533888, Fax: +604-6579150 wongcj usm.my, mjafrigusm.my, khiruddgusm.my, hslimgusm.my, bennywtggmail.com
Abstract This paper reports12 that a digital single-lens reflex (SLR) camera can be applied as a remote sensor for monitoring the concentrations of particulate matter less than 10 micron (PM1o). An algorithm was developed based on the regression analysis of relationship between the measured reflectance and the reflected components from a surface material and the atmosphere. This algorithm converts multispectral image pixel values acquired from this camera into quantitative values of the concentrations of PM1o. These computed PM1o values were compared to other standard values measured by a DustTrak'TM meter. The correlation results showed that the newly develop algorithm produced a high degree of accuracy as indicated by high correlation coefficient (R2) of 0.75 and low root-meansquare-error (RMS) of ±5 Rg/mg3.
lungs. This effect has been linked to respiratory disease, cancer and other potentially deadly illnesses. In order to give warning to public for long exposure to this type of harmful air pollution that causes adverse health effects, the objective of this study was developed efficient techniques to monitor the concentrations of particulate matter less than 10 micron (PM1o). An algorithm was developed based on the atmospheric characteristic for visible wavelength band data. The proposed algorithm was used to determine air quality by using the multispectral image pixel values acquired from a digital SLR camera.
2. BRIEF DESCRIPTION OF THE ALGORITHM We developed a new algorithm for generating a unique image processing mechanism. This algorithm was developed based on the fundamental optical theory, such as light absorption, light scattering and light reflection. This is a modified version of the model developed by other researcher [1, 2], which uses the skylight to indicate the existence of haze. Skylight is an indirect radiation, which occurs when the radiation from the sun is scattered by elements within the air pollutant column. Figure 1 shows electromagnetic radiation path propagating from the sun towards the digital camera penetrating through the atmospheric pollutant column.
TABLE OF CONTENTS 1. INTRODUCTION...................................1 2. BRIEF DESCRIPTION OF THE ALGORITHM ..........1 METHODOLOGY 3. ...................................3 DATA A4.NALYSIS AND RESULTS............................3 5. CONCLUSION...................................5 ACKNOWLEDGEMENTS ...................................5 REFERENCES ......... ..........................5 BIOGRAPHY ........ ..........................5
1. INTRODUCTION
The reflectance recorded by the sensor of a SLR camera is
There has been a growing interest to sub-micrometer particles for two main reasons. First, such particles cause public health concern, and second, atmospheric aerosol particles have been shown to have a significant influence on atmospheric quality and climate at a local and global scale. In particular, particle mass concentrations and size distributions are of crucial importance to the aerosol's impact on human health. Numerous scientific studies have indicated that the most harmful component in air pollution is the microscopic dust with diameter less than 2.5 micrometers (PM2.5) [1]; however, monitoring of PM1o gives a bigger tolerance. This is due to their ability to penetrate deep into the lungs and embed themselves in the
Rs =Rref + Ratm
where Rref is the reflectance from a known reference (in this case, a distant green vegetation), and Ratm is the reflectance from atmospheric scattering.
1 1 2
(1)
1-4244-1488-1/08/$25.00 C 2008 IEEE. IEEEAC paper #1485, Version 2, Updated Oct 22, 2007
1
where Ratm is atmospheric reflectance, Ra is particle reflectance, R, is molecule reflectance By substituting equation (2) and equation (3) into equation (4), we obtain Ratm
The optical depth, X given by Camagni and Sandroni [8] as expressed in equation (6) and equation (7).
u spernd
Partictular
Radiation Sun Light
I
-
_talto
Directed Tcwards DitlhCame~ra I
T
6r rrad
n
i* Di
i
%
_
(5)
IraPa (e)) + 'rrPr (0e)]
4,usgv
= ups
(6)
Iaradiatlon From
RotrKnown Rrent"ee
where x is optical depth, is finite path.
lCmr
a
is absorption, p is density and s
rritinatloi Not
$ antoid
Directed ToWardS
AtmoSptkit
and
Polluant Column
Figure 1 - The electromagnetic radiation propagates from sunlight towards the known reference, and then reflected to propagate towards the internet surveillance camera penetrating through the interaction in atmospheric pollutant column.
r
r
A
,.
(2)
IaPa (j) A
,.
..
(7)
r
=
rp
=
aUrPrS
(8a) (8b)
ppp s
(9)
[5aPaPa(e) + a,PrPr (e))]
Ratm, Ca, Ur, P a () ) and Pr( ) )
are
dependent
on
wavelength, X, thus equation (9) can be expressed as
Ratm (2) = 4/ Fu [Q7i (2)Pa%P (0,2) + 07r (2)PrPr (0,2)]
(10) when Pa is particle concentration (PM1O), P and Pr is molecule concentration, G. Equation (10) can be written as equation (1 1)
(3)
where la is aerosol optical thickness, Pa( )) is aerosol scattering phase function.
Ratm (A) =
Atmospheric reflectance, Ratm is the sum of reflectance from particles, R, and reflectance from molecules, R, as shown by Vermote, et al. [7]. Ratm = Ra + Rr
Z'r
Ratm 4s,=
We assume that the atmospheric reflectance due to particle, Ra, is also linear with the la as shown by King, et al. [5] and Fukushima, et al. [6]. This assumption is valid because Liu, et al., [4] also found the linear relationship between both aerosol and moleculur scattering. =
'
Equations (8) are substituted into equation (5).
where xc is the aerosol optical thickness (Molecule), Pr( ) ) is the Rayleigh scattering phase function, pt, is the cosine of viewing angle, and pt, is the cosine of solar zenith angle.
Ra
r a +
The optical depths for the atmosphere particles and molecules can be written as equation (8)
The atmospheric reflectance due to molecule, Rr, is given by Liu, et al.[4], T r Pr (e))
=
4psyv
[5a (A)PR (04A) + ar (A)GfI. (E04i)] (1 1)
The result was extended to a two bands algorithm as shown in equation (12).
(4)
2
RatmQii)
S
[5a(Ai)PPa(O,Ai)+QQLi)GPr(QAi)]
Study Area
The SLR camera was used to capture images within the premises of Universiti Sains Malaysia's campus at longitude of 1000 17.864' and latitude of 50 21.528' as shown in Figure 3. We used green vegetation as our reference target (Figure 3 & Figure 4).
(12a) Ratm (a2 ) =
Ppa ( )
+a
GP, (E)] (12b)
where Ratjki) is atmospheric reflectance, i band numbers.
=
1, 2 are the
Solving equation (12a) and (12b) simultaneously and we obtain particle concentration of PM1o, P as P = aO Ratm (21 ) +
(13)
alRatm (22)
where aj is algorithm coefficients, j empirically determined.
=
0, 1 are then Aiiai:
Form the equation (13); we found that PM1o was linearly related to the reflectance for band 1 and band 2. This algorithm was generated based on the linear relationship between x and reflectance. Retalis et al. [9] also found that the PM1o was linearly related to x and the correlation coefficient for the linear model was better than exponential. This means that reflectance was linear with the PM1o. In order to simplify the data processing, the air quality concentration was used in our analysis instead of using density, p, values.
Figure 3 - Location of the SLR camera captured images at the top floor of School of Physics, Universiti Sains Malaysia. R4rence Tarmo
3. METHODOLOGY
Equipment Set-Up A digital Canon SLR camera, designed as air quality remote monitoring sensor, was used for monitoring the concentrations of particles less than 10 micrometers in diameter. It is a Canon EOS 400D camera and Figure 2 shows this SLR camera used in this project.
Figure 4 - The digital image captured by the SLR camera at the top floor of School of Physics, Universiti Sains Malaysia.
4. DATA ANALYSIS AND RESULTS Camera Calibration We calibrated the SLR camera using a spectroradiometer with known light source and color papers. This calibration enabled us to convert SLR camera digital numbers (DN) to irradiance units. The coefficients of calibrated SLR camera are
y, = 0.0003x1 + 0.0342 Y2 = 0.0005x2 + 0.0286 y3 = 0.0004x3 + 0.023 1 Figure 2 - A Canon SLR camera used in this study.
3
(14) (15) (16)
where Yi is irradiance for red band (WMn2 nm-'), Y2 is irradiance for green band (Wm-2 nm-'), y3 iS irradiance for blue band (WMn2 nmn'), x1 is digital number for red band, x2 is digital number for green band and X3 is digital number for blue band.
TABLE I REGRESSION RESULTS USING DIFFERENT FORMS OF ALGORITHMS FOR PMlO
PM1o =ao+a±B1+a2B1 PM1o ao+a±B2+a2B22 PM1o =ao+a±B3+a2B32 PM1o ao+allnB1+a2(lnB1)2 PM1o =ao+al nB2+a2(lnB2)2 PM1o =ao+allnB3+a2(lnB3)2 PM1o =ao+a1(BI B3)+a2(B1 B3)2 PM10 =ao+a(BI/B2)+a2(BI/B2)2 PM1o =ao+a1(B2/B3)+a2(B2/B3)2 PM1o =ao+ajln(Bj/B3)+a2ln(Bj/B3)2 PM0o =ao+aln(BI/B2)+a2ln(B /B2)
Figure 4 shows a sample from the digital images captured by the SLR camera. The target of interest is the green vegetation grown on a distant hill. Digital images were separated into three bands (red, green and blue). Digital numbers (DN) of the target were determined from the digital images for each band. Equations 14, 15 and 16 were used to convert these DN values into irradiance. Determine Algorithm
Coefficients and Air Quality
A handheld spectroradiometer was used to measure the sun radiation at the ground surface. The reflectance values record by the sensor was calculate using equation (17) below. R
=-
y(A)
Es (A)
2
Algorithm
15 16 13 18 17 12
PM1o=ao+a1(B2-B3) +a2(B2-B3)2 PM1o =ao+a1(B -B3) +a2(B1 -3)2 PM1o =ao+aB±l+a2B2+a3B3
0.5158 0.1109 0.6405 0.5958 0.1197 0.6413 0.5348 0.3327 0.0196 0.4713 0.3205 0.0428 0.1424 0.1340 0.3199 0.0638 0.4624 0.1985 0.0770 0.0265 0.0487 0.7628
PM10 =a0B1+a1B3 (Proposed)
0.7628
7
PM1o=ao+alln(B2/B3)+a2ln(B2/B3)2 PM1o =ao+a(BI-B2)/B3+ a2((BI-B2)/B3)2 PM1o =ao+a(BI-B3)/B2+ a2((B1-B3)/B2)2 PM1o=ao+a(B2-B3) Bl+ a2((B2-B3)/B1)2 PM10 =ao+a1(B+B3)/B2+ a2((B1+B3)/B2) PM10 =ao+a(B2+B3) Bl+ a2((B2+B3)/B1)2 PM10 =ao+a1(B1+B2)/B3+ a2((B1+B2)/B3)2 PM1o=ao+a1(B2-B1) +a2(B2- 1)2
(17)
where y(X) is irradiance of each visible bands recorded by the camera (Wm-2 nm-') [can be determined by equation (14), (15), (16)] and Es(X) is sun radiation at the ground surface using a hand held spectroradiometer (Wm-2 nMn1). The reflectance recorded by the SLR camera was subtracted by the reflectance of the known surface [equation (1)] to obtain the reflectance caused by the atmospheric
RMS ( mg/mr)
*
13 15 18 14
15 18 17 17 15 17 14 17 18 18 18 9
B1, B2 and B3 are the reflectance for red, green and blue band respectively
components.
Ratm= Rs -Rref
(18)
Air quality of PM1o can be determined by the DustTrak meter. The relationship between the atmospheric reflectance and the corresponding air quality data for the pollutant was established by using regression analysis as shown in Table 1. Thus, algorithm coefficients in equation (13) can be determined to calculate the air quality of PM1o. Figure 5
'k ._
A,
la
- H...-
9.wI
A
ll.
il
shows the temporal development of real time air quality of
PM1o in a day measured by the SLR camera and DustTrak
meter. The data were obtained on 7 May 2007 from 7.00am to 6.00pm.
Time
Figure 5 - Graph of PM10 concentration versus Time (7 May 2007).
Validation of the Measurements The accuracy of air quality measured by SLR camera can be validated through correlation between the estimated measurement from the internet surveillance camera and the actual measurement from DustTrak meter as shown in Figure 6. 4
[2] 7a
[3]
R' = 0.75 RMS = 5 (hm
[4]
.4 ,
+
4S h
40 E
F5 G _O
a
2...
£R
[5]
oI
;~~~S
[6]
=1
I20 10
[7] 0
1o
20
NAtRAr
30
40
so
so
Meter Measurement
Co
[8]
qtg/mr)
[9]
Figure 6 Correlation coefficient and RMS error of the measured and estimated PM1o (ptg/m3) values for calibration analysis of the SLR camera -
S.G. Narasimhan and S.K. Nayar, "Vision and the Atmosphere," International Journal on Computer Vision, Vol.48 (3), 2002, pp.233-254, 2002.
W. E. K. Middleton, "Vision through the atmosphere", University of Toronto Press, Toronto. 1952. C. H. Liu, A. J. Chen and G. R. Liu, "An image-based retrieval algorithm of aerosol characteristics and surface reflectance for satellite images," International Journal of Remote Sensing, Vol. 17 (17), pp. 3477-3500, 1996. M. D. King, Y. J. Kaufman, D. Tanre, and T. Nakajima, "Remote sensing of tropospheric aerosold form space: past, present and future," Bulletin of the American Meteorological society, pp. 22292259, 1999. H. Fukushima, M. Toratani, S. Yamamiya, and Y. Mitomi, "Atmospheric correction algorithm for ADEOS/OCTS acean color data: performance comparison based on ship and buoy measurements." Adv. Space Res, Vol. 25, No. 5, pp. 1015-1024, 2000. E. Vermote, D. Tanre, J. L. Deuze, M. Herman, and J. J. Morcrette, "6S user guide Version 2, Second Simulation of the satellite signal in the solar spectrum (6S)", 1997. http:Hwww.geog.tamu.edu/klein/geog66 1/handouts/6s/6smanv2.O P1.pdf. P. Camagni and S. Sandroni, "Optical Remote sensing of air pollution," Joint Research Centre, Ispra, Italy, Elsevier Science Publishing Company Inc. 1983. A. Retalis, N. Sifakis, N. Grosso, D. Paronis, and D. Sarigiannis, "Aerosol optical thickness retrieval from AVHRR images over the Athens urban area" 2003. http:Hsat2.space.noa.gr/rsensing/documents/IGARSS2003_AVHRR_ Retalisetal_web.pdf
The correlation coefficient (R2) produced by the internet surveillance camera data set was 0.75. The RMS value for SLR camera was ±5 ptg/M3.
BIOGRAPHY Wong Chow Jeng is senior lecturer at School at the Universiti Sains Malaysia, Malaysia. He teaches the courses
5. CONCLUSION
of Physics,
This study has shown that by using image processing technique with new developed algorithm, SLR camera can be used as temporal air quality remote monitoring sensor. This technique uses relatively inexpensive equipment and it is easy to operate compared to other air pollution monitoring instruments. This showed that the SLR camera imagery gives an alternative way to overcome the difficulty of obtaining satellite image in the equatorial region and provides air quality information.
of
design
engineering,
fiber
optics
communication and electronics engineering, on the undergraduate and graduate level. He is a member of The International Society for Optical Engineering (SPIE). Over 15 years of working experience in Semiconductors Manufacturing and Electronics Assembly Industry. He have experiences worked as Factory Manager in Varitronix EC - Penang (Subsidiary of Magna DonnellyUSA), as Industrial Engineering Department Manager in SONY Electronics - Penang, as QA Senior Engineer in HITACHI Semiconductor - Penang, and as Research & Development Engineer in SIEMENS Semiconductor Penang. He received his M.Sc. in Plasma Physics and B.Sc. (Hons.) in Physics from Universiti Malaya, Kuala Lumpur, Malaysia. In 1990, he successfully redesigned, built and tested a plasma focus fusion device for ICTP (International Centre of Theoretical Physics) in Italy. He also successfully carried out some researches in ICTP. Currently, his research interests include remote sensing application for air pollution monitoring via networking. Presently, he is perusing his PhD by using Internet Protocol Camera and working on remote sensing algorithm for retrievals of air quality information. In year 2006, this project was chosen to participate in the competition of 17th International Invention, Industrial Design & technology Exhibition 2006 at KLCC, Kuala Lumpur. This project won three awards in this competition. Our team beat entries from South Korea,
ACKNOWLEDGEMENTS This project was carried out using the Universiti Sains Malaysia short term grants and Science Fund from Ministry of Science, Technology and Innovation, Malaysia. We would like to thank the technical staff who participated in this project. Thanks are also extended to USM for support and encouragement.
REFERENCES [1] D. W. Dockery, C. A. Pope, X. Xu, J. D. Spengler, J. H. Ware, M. E. Fay, B. G. Ferris, and F. E. Speizer, "An Association between Air Pollution and Mortality in Six U.S. Cities", New England Journal of Medicine, Vol.329, No. 24, 1753-1759. 1993.
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Iran, Sri Lanka, Taiwan and Japan to win the World Intellectual Property Organization Awards (WIPO) for International Best Invention. The second award is Malaysia Best Invention called Kandaya & Associates Award (KASS), and then the third award is ITEX Gold Awardfor invention.
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