The characterization of aquatic pollution using correlative analysis of physico-chemical parameters and data from the IRS1C satellite: Application to Oran city, Algeria
Population growth in developing countries has led to a rapid expansion of primary urban areas. Solid and liquid wastes coming from domestic consumption and industrial activities are discharged into potential water sources such as seas, lakes and other natural areas. In order to protect these areas and to control the pollution caused by such waste, it is necessary to continuously monitor these zones. Satellite imagery, such as that obtained with the IRS1C satellite, can be used to estimate, with reasonable accuracy, the factors affecting water quality. This technique allows for the necessary continuous monitoring of impacted areas and affords an overall analysis of their degree of pollution. Waste disposal affects and alters the chemical and physical characteristics of water. Moreover, water quality could be altered by the decay products of extracellular release and death of aquatic organisms. In turn, these changes can cause an alteration in the appearance of water. It is therefore reasonable to look for relations linking variations in chemical and physical properties to variations in the spectral properties of water, or more precisely, to its reflecting power. The aim of the present study was to: 1. relate the reflectance of polluted water to its physico-chemical parameters; 2. show the significance of such relationships. Water samples were collected from different sites: 1. two outlets where sewage of Oran City is being emptied into the sea; 2. far from these outlets; 3. far from the port; 4. far from two sites in a lake known to be subjected to both urban and industrial waste. From each site, water samples were taken at the source and from different places far from the coast. The following water quality parameters were analyzed: temperature, acidity, turbidity, suspended material, dissolved oxygen, electrical conductivity, chemical oxygen demand and 5-day biological oxygen demand. The reflectance coefficient of water in each of the studied areas was calculated using the IRS1C image at four bands. The satellite observes the earth in four spectral channels: C1 (0.45- 0.52 m); C2 (0.52 - 0.59 m); C3 (0.62 - 0.68 m) and C4 (0.77 - 0.86 m) with a spatial resolution of 6 m. The radiance measured by the satellite sensor results from solar radiation affected by several processes including absorption and diffusion on both downward and upward paths by the atmospheric components, and reflection at the ground surface. We first simulated the measurement achieved by the captor of our reference water (from the sea far from any pollution). Secondly, we used imagery treatment to determine the real value evaluated by the satellite for deep-sea water. We used both a simulated value and the real value to calculate the calibration factor for each channel. We took the image and transformed the digital account into radiance by linear relationships. For each channel, we use the reverse model to calculate the reflectance for each pixel. The substances that determine the optical properties of water surfaces, and thus influence their reflectivity, may be classified into three categories: 1. living phytoplankton and the associated detritus; 2. suspended particles; 3. dissolved organic matter. The phytoplankton and the associated biogenic detritus generally have the same colour. In most oceanic waters, and in some coastal waters where terrigenous supplies are unimportant, the influence of phytoplankton is dominant. In natural conditions, it is very difficult to dissociate the influences of phytoplankton and those of biogenic detritus on the coefficient of absorption, for which only global estimations are made. The phytoplankton cells and the particles corresponding to biogenic detritus cause a Mie diffusion of light, which is relatively independent of the wavelength. Therefore, the colour of water gradually turns green with increasing phytoplankton concentration. As expected, our results demonstrated that for polluted waters there was a good correlation between turbidity and concentrations of suspended material. Turbidity and suspended solids have a common effect in reducing light penetration, thereby suppressing primary production in the form of algae and macrophytes. This decrease, in turn, affects the available dissolved oxygen. Our results confirmed this situation by showing a highly negative correlation between turbidity and dissolved oxygen. The oxygen needed for chemical oxidation of organic matter and the accompanied minerals is expressed as COD. Therefore, higher values of this parameter means more organic pollution. BOD5 estimates the oxygen needed for biological oxidation of organic and inorganic matter by organisms that are actually present in the polluted water. Therefore, the DCO/DBO5 ratio refers to the capacity of organisms found in the water to oxidize the organic matter found in the medium. Our results showed that this ratio increased with increasing pollution and with reflectance in the different channels. For easier water quality monitoring we could use the satellite imagery to estimate, with excellent validity, the capability of the water to reduce organic pollution resulting from urban discharge. Moreover, biological parameters could be calculated from each other since there was a high correlation found among them. The correlation between reflectance and the biochemical parameters was higher for channels C2 and C3 than for the other two channels. The following correlations between reflectance and the measured parameters chemical were obtained: oxygen demand, r = 0.84; suspended matter, r = 0.88; 5-day biological oxygen demand, r = 0.62; dissolved oxygen, r = 0.77; turbidity, r = 0.90. Finally, linear relationships were established between physico-chemical parameters and reflectance values. The inversion of these relationships offered the possibility to estimate for each pixel the degree of water quality. Figures showed clearly different distinct colour sub-areas in each of the studied areas. Each colour indicated a different degree of water quality or pollution. With this technique it was possible to construct, relatively rapidly, a global picture describing the degree of unknown pollution spread over a wide water surface.