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Internet Surveillance Camera Measurements of Atmospheric Aerosols Concentration

Written By

C.J. Wong, M.Z. MatJafri, K. Abdullah and H.S. Lim

Published: February 1st, 2010

DOI: 10.5772/9099

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1. Introduction

Nowadays, air pollution becomes a very serious problem with the rapid growth of industrialization and urbanization (Kim Oanh et al., 2006, Wu et al., 2006). This air pollution is not only continues to damage our environment, it also endanger our health (Pope et al., 2008, Pope et al., 2007, Banauch et al., 2006, Brunekreef et al., 2002). Evidence gathered to date indicates that the most harmful component of this pollution is the microscopic atmospheric aerosols with an aerodynamic diameter below 10 micrometers (PM10) (Pope et al., 2008, Pope et al., 2007, Pope et al., 2004, Donaldson et al., 2000, Pope et al., 1995). Only particles less than 10 micrometers in diameter can be inhaled deep into the lungs, then embed themselves in the lungs to cause adverse health effects. These effects have been linked to respiratory disease, cancer and other potentially deadly illnesses. This is the reason for both the WHO and the United Nations have declared that atmospheric aerosols poses the greatest air pollution threat globally.

In order to monitor the levels of air pollution, so that early warning will be provided to prevent long exposure to this type of harmful air pollution. Many researchers attempt to develop more efficient techniques to monitor this atmospheric aerosols air pollution. This includes the techniques of Atmospheric Optical Thickness (AOT) and satellite images (Hadjimitsis, 2009, Hadjimitsis, 2008, Sifakis et al., 1992, Kaufman et al., 1983, Lim et al., 2009). Satellite images were normally used by researchers in their remote sensing air quality studies, but the main drawback of using satellite images is the difficulty in obtaining cloud-free scenes especially for the Equatorial region.

In order to overcome cloud-free scenes problem, aerial photographic imagery technique is used to obtain air pollution map. This technique utilizes fundamental optical theory like light absorption, light scattering and light reflection. This technique has long been used for visibility monitoring (Middleton, 1968, Noll et al., 1968, Horvath et al., 1969, Diederen et al., 1985). The continuous and rapid evolution of digital technologies in the last decade fostered an incredible improvement in digital photography technology, in information and communication technologies (ICT) and personal computer technology. This modern digital technology allows image data transfer over the internet protocol, which provides real time observation and image processing (Wong et al., 2009, Wong et al., 2007). This has made it possible to monitor real time PM10 air pollution at multi location. This is an attempt to fulfill the need for preventing long exposure to this harmful air pollution.

The object of this study is to develop a state-of-the-art technique to enhance the capability of the internet surveillance camera for temporal air quality monitoring. This technique is able to detect particulate matter with diameter less than 10 micrometers (PM10). An empirical algorithm was developed and tested based on the atmospheric characteristic to determine PM10 concentrations using multispectral data obtained from the internet surveillance camera. A program is developed by using this algorithm to determine the real-time air quality information automatically. This development showed that the modern Information and Communications Technologies (ICT) and digital image processing technology could monitor temporal development of air quality at multi location simultaneously from a central monitoring station.

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2. Description of the Algorithm

In this study, we developed an algorithm based on the fundamental optical theory, that is light absorption, light scattering and light reflection. This algorithm is used to perform image processing on the captured digital images to determine the concentration of atmospheric aerosols.

Figure 1.

The skylight parameter model to illustrate 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.

Figure 1 shows the electromagnetic radiation path of ambient light propagating towards the internet surveillance camera, and then this electromagnetic radiation is reflected by a known reference target and penetrating through the ambient pollutant column. At the ambient pollutant column, this electromagnetic radiation encounters absorption and scatters. In a single scattering of visible electromagnetic radiation by aerosol in atmosphere, Liu et al. showed that the atmospheric reflectance due to molecules scattering, R r is proportional to the optical thickness for molecules, τ r (Liu et al., 1996). This atmospheric reflectance due to molecule scattering, R r can be written as

R r = τ r P r ( Θ ) 4 μ s μ ν E1

where P r (Θ) is the scattering phase function for molecules, µv is the cosine of viewing angle, and µs is the cosine of solar zenith angle.

In the same paper, Liu et al. also showed that the atmospheric reflectance due to particles scattering, R a is proportional the optical thickness for aerosols, τ a (Liu et al., 1996). Later on, King et al. and Fukushima et al. have further confirmed this relationship (King et al., 1999, Fukushima et al., 2000). This particles scattering, R a is

R a = τ a P a ( Θ ) 4 μ s μ ν E2

where P a (Θ) is scattering phase function for aerosols.

In year 1997, Vermote et al. showed that the atmospheric reflectance, R atm is the sum of reflectance from particles, R a and reflectance from molecules, R r (Vermote et al., 1997). This atmospheric reflectance, R atm can be written as

R a t m = R a + R r E3

By substituting equation (1) and equation (2) into equation (3), the atmospheric reflectance, R atm also can be written as

R a t m = 1 4 μ s μ v [ τ a P a ( Θ ) + τ r P r ( Θ ) ] E4

Camagni et al. expressed the optical depth, τ in term of absorption, σ and finite path, s (Camagni et al., 1983). Equation (5) showed this optical depth, τ as

τ = σ ρ s E5

where σ is absorption, ρ is density and s is finite path.

In the same paper, Camagni et al. also showed that this optical depth, τ is the sum of the optical depth for particle aerosols, τ a and the optical depth for molecule aerosols, τ r (Camagni et al., 1983). This optical depth, τ also can be written as

τ = τ a + τ r E6

As the optical depths for particle aerosols, τ a and for molecule aerosols, τ r can be written in the form of equation (5). Thus the optical depths for particle aerosols, τ a and for molecule aerosols, τ r are written as

τ a = σ a ρ a s E7
τ r = σ r ρ r s E8

Equations (7) and (8) are substituted into equation (4). The atmospheric reflectance, R atm become

R a t m = s 4 μ s μ v [ σ a ρ a P a ( Θ ) + σ r ρ r P r ( Θ ) ] E9

R atm , σ a , σ r , P a (Θ) and P r (Θ) are dependent on wavelength, λ, thus equation (9) can be expressed as

R a t m ( λ ) = s 4 μ s μ v [ σ a ( λ ) ρ a P a ( Θ , λ ) + σ r ( λ ) ρ r P r ( Θ , λ ) ] E10

when ρ a is particle aerosols concentration (PM10), P and ρ r is molecule aerosols concentration, G. Equation (10) can be written as

R a t m ( λ ) = s 4 μ s μ v [ σ a ( λ ) P P a ( Θ , λ ) + σ r ( λ ) G P r ( Θ , λ ) ] E11

Equation (11) is extended into a two bands algorithm for wavelength, λ1 and wavelength, λ2. These two bands algorithm are as shown in equation (12) and equation (13).

R a t m ( λ 1 ) = s 4 μ s μ v [ σ a ( λ 1 ) P P a ( Θ , λ 1 ) + σ r ( λ 1 ) G P r ( Θ , λ 1 ) ] E12
R a t m ( λ 2 ) = s 4 μ s μ v [ σ a ( λ 2 ) P P a ( Θ , λ 2 ) + σ r ( λ 2 ) G P r ( Θ , λ 2 ) ] E13

where R atmi) isatmospheric reflectance, i = 1, 2 are the band numbers.

Solving equation (12) and (13) simultaneously and we obtain particle concentration of PM10, P as

P = a 0 R a t m ( λ 1 ) + a 1 R a t m ( λ 2 ) E14

where aj is algorithm coefficients, j = 0, 1 are then empirically determined.

From the equation (14); the PM10 concentration is linearly related to the atmosphere reflectance for band 1 and band 2. This algorithm was generated based on the linear relationship between τ and reflectance. Retalis et al. also found that the PM10 was linearly related to τ and the correlation coefficient for the linear model was better than exponential (Retalis et al., 2003). This means that reflectance was linear with the PM10. In order to simplify the data processing, the air quality concentration was used in our analysis instead of using density, ρ, values.

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3. Methodology

3.1. Equipment Set-Up

As shown in Figure 2, an internet surveillance camera was used as remote sensing sensor to monitor the concentrations of particles less than 10 micrometers in diameter. This internet surveillance camera is a Bosch’s auto dome 300 series PTZ camera system. It is a 0.4 mega pixel (PAL) Charge-Couple-Device CCD camera, which allows image data transfer over the standard computer networks (Ethernet networks), internet. Therefore it can be used as a remote sensing sensor to monitor air quality.

Figure 2.

A 0.4 mega pixel (PAL) Charge-Couple-Device CCD, internet surveillance camera used in this study is a Bosch’s auto dome 300 series PTZ camera system.

This internet surveillance camera was calibrated by using a spectroradiometer with Pro Lamp light source and colour papers. This calibration enabled us to convert the digital numbers (DN) of the images captured by the internet surveillance camera to irradiance. The coefficients of calibrated internet surveillance camera are as listed below

L R = 0.0003 N R + 0.0278 E15
L G = 0.0004 N G + 0.0263 E16
L B = 0.0004 N B + 0.0248 E17

where L R is irradiance for red band (Wm-2 nm-1), L G is irradiance for green band (Wm-2 nm-1), L B is irradiance for blue band (Wm-2 nm-1), N R is digital number for red band, N G is digital number for green band and N B is digital number for blue band.

The schematic set-up of the internet surveillance camera is shown in Figure 3. This set-up provides a continuous, on-line, real-time monitoring for air pollution at multiple locations. It is able to detect the present of particulates air pollution immediately, in the air and helps to ensure the continuing safety of environmental air for living creatures.

Figure 3.

The schematic set-up of internet surveillance camera as remote sensor to monitor air quality.

3.2. Study Location

The internet surveillance camera was installed at the top floor of the Chancellery building, Universiti Sains Malaysia’s campus. It is located at longitude of 100 18‘20.67“ and latitude of 5 21‘28.50“ as shown in Figure 4 and Figure 5. This internet surveillance camera is looking to the direction of the Penang bridge (Figure 5). As shown in Figure 5 and Figure 6, the reference target that we used in this study is green vegetation.

Figure 4.

The internet surveillance camera is installed at the top floor of Chancellery building in Universiti Sains Malaysia (USM).

Figure 5.

The satellite image showed the location of internet surveillance camera capture photograph and the location of the reference target.

Figure 6.

The reference target of green vegetation captured by the internet surveillance camera.

Figure 6 shows a sample from the digital images captured by the IP 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 9, 10 and 11 were used to convert these DN values into irradiance.

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4. Determine Algorithm Coefficients and Atmospheric Aerosol Concentration

A handheld spectroradiometer was used to measure the sun radiation at the ground surface. The reflectance values recorded by the sensor was calculate using equation (18) below.

R S = L ( λ ) E ( λ ) E18

where L(λ) is irradiance of each visible bands recorded by the internet surveillance camera (Wm-2 nm-1) [can be determined by equation (15), (16), (17)] and E(λ) is sun radiation at the ground surface measured by a hand held spectroradiometer (Wm-2 nm-1).

From the skylight model showed in Figure 1, the reflectance recorded by the internet surveillance camera (R s ) was subtracted by the reflectance of the known surface (R ref ) to obtain the reflectance caused by the atmospheric components (R atm ).

R S R r e f = R a t m E19

The DustTrak meter used to determine atmospheric aerosol concentration of PM10. The relationship between the atmospheric reflectance and the corresponding atmospheric aerosol concentration data for the pollutant was established by using regression analysis as shown in Table 1. Thus, algorithm coefficients in equation (14) can be determined to calculate the atmospherics aerosol concentration of PM10.

Algorithm R 2 RMS ( µg/m 3 )
P M 10 = a 0 + a 1 R 1 + a 2 R 1 2 0. 5662 12
P M 10 = a 0 + a 1 R 2 + a 2 R 2 2 0.2238 14
P M 10 = a 0 + a 1 R 3 + a 2 R 3 2 0.4627 17
P M 10 = a 0 + a 1 ln R 1 + a 2 ( ln R 1 ) 2 0.4536 17
P M 10 = a 0 + a 1 ln R 2 + a 2 ( ln R 2 ) 2 0.1426 16
P M 10 = a 0 + a 1 ln R 3 + a 2 ( ln R 3 ) 2 0.5129 13
P M 10 = a 0 + a 1 ( R 1 / R 3 ) + a 2 ( R 1 / R 3 ) 2 0.3196 15
P M 10 = a 0 + a 1 ( R 1 / R 2 ) + a 2 ( R 1 / R 2 ) 2 0.3243 14
P M 10 = a 0 + a 1 ( R 2 / R 3 ) + a 2 ( R 2 / R 3 ) 2 0.2983 15
P M 10 = a 0 + a 1 ln ( R 1 / R 3 ) + a 2 ln ( R 1 / R 3 ) 2 0.5326 16
P M 10 = a 0 + a 1 ln ( R 1 / R 2 ) + a 2 ln ( R 1 / R 2 ) 2 0.4283 12
P M 10 = a 0 + a 1 ln ( R 2 / R 3 ) + a 2 ln ( R 2 / R 3 ) 2 0.2734 16
P M 10 = a 0 + a 1 ( R 1 R 2 ) / R 3 + a 2 ( ( R 1 R 2 ) / R 3 ) 2 0.3834 17
P M 10 = a 0 + a 1 ( R 1 R 3 ) / R 2 + a 2 ( ( R 1 R 3 ) / R 2 ) 2 0.4273 18
P M 10 = a 0 + a 1 ( R 2 R 3 ) / R 1 + a 2 ( ( R 2 R 3 ) / R 1 ) 2 0.3826 16
P M 10 = a 0 + a 1 ( R 1 + R 2 ) / R 3 + a 2 ( ( R 1 + R 2 ) / R 3 ) 2 0.4826 16
P M 10 = a 0 + a 1 ( R 1 + R 3 ) / R 2 + a 2 ( ( R 1 + R 3 ) / R 2 ) 2 0.5372 17
P M 10 = a 0 + a 1 ( R 2 + R 3 ) / R 1 + a 2 ( ( R 2 + R 3 ) / R 1 ) 2 0.6532 15
P M 10 = a 0 + a 1 ( R 2 R 1 ) + a 2 ( R 2 R 1 ) 2 0.6215 17
P M 10 = a 0 + a 1 ( R 2 R 3 ) + a 2 ( R 2 R 3 ) 2 0.3782 16
P M 10 = a 0 + a 1 ( R 1 R 3 ) + a 2 ( R 1 R 3 ) 2 0.4725 13
P M 10 = a 0 + a 1 R 1 + a 2 R 2 + a 3 R 3 0.7321 9
P M 10 = a 0 R 1 + a 1 R 3 (Proposed) 0.7852 6
* R 1 , R 2 and R 3 are the reflectance for red, green and blue band respectively for PM 10

Table 1.

Regression results using different forms of algorithms to determine algorithm coefficients.

Figure 7 shows three photographs of Penang Bridge at different atmospheric aerosol concentration level. These photographs were captured at around 10.30 am to 11.00 am but on different date. Photograph at Figure 7 (a) was captured during low atmospheric aerosol concentration. This atmospheric aerosol concentration level can be determined from the equation (14) after we determine the algorithm coefficients. The atmospheric aerosol concentration level for photograph at Figure 7 (a) is 34 ± 6 µg/m3.

Figure 7.

Three photographs of Penang Bridge at different atmospheric aerosol concentration level.

For Figure 7 (b) and Figure (c), the atmospheric aerosol concentration levels are 56 ± 6 µg/m3 and 93 ± 6 µg/m3 respectively.

The relationship between the atmospheric reflectance and the corresponding atmospheric aerosol concentration data for the pollutant was established by using regression analysis. The correlation coefficient (R2) between the predicted and the measured PM10 values, and root-mean-square-error (RMS) value were determined. Figure 8 shows the correlation between the estimated measurement of atmospheric aerosol concentration by the internet surveillance camera and the measurement of atmospheric aerosol concentration by the DustTrak meter.

Figure 8.

Correlation coefficient and RMS error of the measured and estimated PM10 (µg/m3) values for the internet surveillance camera.

The correlation coefficient (R2) produced by the internet surveillance camera data set was 0.791. The RMS value for internet surveillance camera was ± 8 µg/m3.

Figure 9 shows the temporal development of real time air quality of PM10 in a day measured by the internet surveillance camera and DustTrak meter. The data were obtained on 21 Ju1 2008 from 8.00am to 5.00pm.

Figure 9.

Graph of atmospheric aerosol concentration concentration versus Time (21 Jul 2008).

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5. Conclusion

This study has shown that by using image processing technique with new developed algorithm, internet surveillance camera can be used as temporal air quality remote monitoring sensor. It produced real time air quality information with high accuracies. This technique uses relatively inexpensive equipment and it is easy to operate compared to other air pollution monitoring instruments. This showed that the internet surveillance camera imagery gives an alternative way to overcome the difficulty of obtaining satellite image in the equatorial region and provides real time air quality information.

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Acknowledgments

This project was supported by the Ministry of Science, Technology and Innovation of Malaysia under Grant 01-01-05-SF0139 “Development of Image Processing Technique via Wireless Internet for Continuous Air Quality Monitoring”, and also supported by the Universiti Sains Malaysia under short term grant “Membangunkan Algorithma Untuk Pengesanan Pencemaran Udara Melalui Rangkaian Internet”. We would like to thank the technical staff who participated in this project. Thanks are also extended to USM for support and encouragement.

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Written By

C.J. Wong, M.Z. MatJafri, K. Abdullah and H.S. Lim

Published: February 1st, 2010