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Water Pollution

Categories Nature, Physics, Pollution, Remote Sensing, Science, Water

Essay, Pages 8 (1845 words)



Essay, Pages 8 (1845 words)

The last few decades have witnessed an increase in the frequency and magnitude of the occurrences of algal blooms in coastal regions. Depending on the area of occurrence, algal blooms results in serious damages and stay as a complex phenomenon to detect due to a high increase in the levels of nutrients (phosphorus and nitrogen) in the water column leads to excessive algal growth, known as eutrophication. Normally the presence of a large amount of algae frequently turns water green or red in the case of Harmful Algal Blooms (HABs).

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Chlorophyll-a is the main indicator of algal blooms and its concentrations are used as indicators of the scale of an algal bloom. Nutrients have a major impact over algal blooms. Due to high levels of human activities surrounding the creek, there have always been eutrophication concerns given the levels of nutrients in the creek. In the last few years, remote sensing algorithms have been developed to monitor algae in water bodies by mapping chlorophyll-a concentrations.

The objective of this study was to map chlorophyll-a in the Dubai Creek from WorldView-2 imagery (high resolution satellite images) and to explore the relationship between chlorophyll-a and other eutrophication indicators. Therefore, a geometrically and atmospherically corrected Worldview-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. WorldView-2 images are significantly useful in mapping of distribution algal blooms in water over its Multispectral counterpart. A spectral model was developed which having the capability to retrieve chlorophyll-a concentrations from the WorldView-2 multispectral image clearly demarcating clear water and water with algal blooms on the base of high resolution images.

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The generated model showed an R-squared value of 0.82 with in-situ chlorophyll-a data as well as another model was developed for the relationship between the levels of spectral chlorophyll-a and total nitrogen and phosphates, which showed an R-squared value of 0.97 with high agreement. Further the generated models are to be used in the mapping of chlorophyll-a, total nitrogen, and phosphates without the need for expensive in-situ monitoring efforts since they will provide values of these parameters everywhere in the creek, hence the models are a unique tool for area extraction.

Key words: Drinking water distribution network, Chlorine Decay, Spatiotemporal analysis, GIS, Infrastructure Integrity

1. Introduction

Since last few decades, Water bodies have been polluted from different sources, affecting the aquatic life in hazardously which in turn may directly or indirectly affect the health of human beings such as oil spills, garbage dumping, discharging of untreated or partially treated wastewater, toxic wastes, algal blooms, and many others. It has witness that the disposal of nutrient rich wastewater is one such threat to ecosystem health where it strengthening of upward trend with the potential of increasing algal bloom growth, known as eutrophication. The presence of nutrients at a level above critical concentrations encourages this rapid algal growth with timely. This tremendous high environmental and human impact should be monitor and manage the occurrence of algal bloom well. Currently point based nutrient monitoring programs are often used to assess its ability to cause eutrophication. However, the monitoring programs cost a significant high economy. However, the monitoring is often limited to a few points over a wide area of waterbodies. The prediction of nutrients throughout a large waterbody using monitoring program is often viewed impractical, even the necessity is well accepted.

The rapid response mapping using satellite remote sensing technology is widely used where increasingly preferred alternative option for emergency assessment, to study water quality, hazards and sources of pollution easily and accurately efforts. However, maps derived from remote sensing observation platforms plays a major role in aiding rapid response emergency operations and long term algal bloom hazard assessment. The presence of algae in water bodies is expected; however, the increase of its concentrations is a source of concern since it acts as an isolating layer that reduces dissolved oxygen concentrations in water bodies [1]. Chlorophyll-a is the green or reddish green pigment found in all plants and is the main indicator of the presence of algae. Other factors that influence algal growth besides nutrients include sunlight and temperature [2]. The coasts of the United Arab Emirates, including that of Dubai, were affected by the catastrophic Harmful Algal Bloom (HAB), commonly known as the red tide in 2008 and 2009. The event, which lasted for a few months over the two years, caused massive fish kills and forced the closure of many beaches [3]. This event emphasized the necessity of research in this area. One study utilized a combination of remote sensing images and in-situ data to perform a multiple linear regression to map chlorophyll-a concentrations along Dubai Creek [4]. Another one attempted mapping of chlorophyll-a concentrations utilizing satellite imagery and a hybrid coordinate ocean numerical model [5]. However, mapping of chlorophyll-a throughout the creek using high resolution satellite imagery was missing.

There have been increases in the frequencies of algal bloom occurrences and magnitudes in the region, mostly caused by anthropogenic factors. The current in-situ methods for monitoring algae are point based and insufficient due to the high cost associated with them. Therefore, remote sensing algorithms have been developed in recent years to monitor algae in water bodies. These algorithms are mostly time- and site-specific, as they are not applicable to different locations or periods of time [6]. Therefore, in order to monitor eutrophication of the creek, new models are needed

The study is to have an accurate and reliable algal bloom growth extraction for a large water body.

The main purpose of this study is to model the spatial distribution of chlorophyll-a and eutrophication indicators in the Dubai Creek. And the specific objectives are as bellow:

· To develop a model with high resolution WorldView-2 satellite imagery and field chlorophyll-a data of Dubai Creek.

· To develop another model using the same imagery that relates chlorophyll-a and eutrophication indicators in Dubai Creek.

2. Study Area

Figure 1. Dubai creek and the water quality monitoring stations (source Google Earth)1

The main focus of this study is Dubai Creek located in between 25° 12′ North latitude to 55° 19′ Eastern longitudes originates in Dubai, the creek divided the city into two main sections- Deira and Bur Dubai post images had been demarcated as the first step of the study. The Dubai Creek extends from the Dubai coast upstream and forms a lagoon downstream. The Creek passes under Al-Gharhoud Bridge and The Floating Bridge and embraces Port Saeed, Ras Al Khor Wildlife Sanctuary, and some malls and hotels.

3. Data preparation

The WorldView-2 satellite image used in this study was acquired on the 24th of July, 2012 and it covered the study area: The Dubai Creek (Figure 2). WorldView-2 images are 11-bit images scaled to 8-bit or 16-bit in eight multispectral bands and one panchromatic band. The pixel size is 2×2 meters in the multispectral bands and 0.5×0.5 meters in the panchromatic band.

Chlorophyll-a data was provided by the Dubai Municipality as quarterly values. Therefore, due to impossibility in obtaining chlorophyll-a concentrations at the exact day of the satellite image acquisition an interpolation of the provided quarterly data was performed so that chlorophyll-a concentrations at the day of the image acquisition could be approximately estimated

3.1 In-situ data

There are 10 monitoring stations located along Dubai Creek that are used by the Dubai Municipality (DM) to record water quality parameters. The DM water quality data used in this study was provided as quarters averages (every three months). The water quality parameters measured by these stations include chlorophyll-a, total nitrogen, nitrates, phosphates, dissolved oxygen (concentration and %), turbidity, pH, and salinity. Figure 2 illustrates the locations of the measurement stations on the WorldView-2 image. Two of the ten monitoring stations located on the creek were excluded from the study. These Hyatt Regency and Al-Gharhoud Bridge monitoring stations was excluded because it was located outside the WorldView-2 satellite image. The Al-Gharhoud Bridge station was excluded because the monitoring station was located under the bridge which means that the pixel representing the location of the station spectrally represents the concrete barrier of the bridge rather than the water column below the bridge. All the modelling and analysis efforts performed in this study were based on the data of the remaining eight stations

4. Methodology

The WorldView-2 image was first reprojected to the Dubai Local Transverse Mercator (DLTM), the DNs were converted to radiance values, and the COST atmospheric correction algorithm was then applied. Then, a band-ratio model to estimate chlorophyll-a concentrations was developed and validated with the interpolated in-situ data. Also, another model was developed to correlate chlorophyll-a and eutrophication indicators

Figure 3. Methodology adopted

5. Preprocessing Worlveiw-2 images data

Before WorldView-2 images were processed to derive chlorophyll-a values in the study area, they were first preprocessed by correcting for geometric, radiometric, and atmospheric errors. Also, chlorophyll-a field data was interpolated based on the assumption that the data of each quarter is a good indication of the midpoint values of the quarter. The following subsections describe the preprocessing procedures implemented on the WorldView-2 images.

WorldView-2 images were delivered free of radiometric errors. Therefore, the DNs (Digital Numbers) were converted to TOA (Top-Of-Atmosphere) spectral radiance without the need for radiometric corrections. The equation utilized for this purpose was provided by Digital Globe and is shown below [7]:

L?pixel,band = (Kband * qpixel,band )/??band —————— (1)


L?pixel,band : is the top-of-atmosphere spectral radiance for a band.

Kband : is the absolute radiometric calibration factor.

qpixel,band : is the radiometrically corrected image pixels.

??band : is the effective bandwidth for a band.

Atmospheric correction was the last step in the pre-processing of satellite images. It was decided to use the COST method to perform atmospheric correction due to its simplicity and the successful results it produces. The mathematical expression of the COST method is as follows [8]:

??pixel,band = (((L?pixel,band – L?haze,band) * dES2 * ?)/(Esun?, band * cos(?s))+0.01—-(2)


??pixel,band: is atmospherically-corrected reflectance.

L?pixel,band: top-of-atmosphere spectral radiance.

dES: Earth-sun distance.

Esun?, band: band-averaged solar spectral irradiance

6. Chlorophyll-a spectral modelling

In order to develop the chlorophyll-a spectral model, different simple band ratios of ground-leaving reflectance values of the eight WorldView-2 bands were used. These ratios were tested against the field values of chlorophyll-a. The different band ratio combinations included two-band, three-band, and four-band ratios. The strength of the correlation between the model and the field values were based on R-squared values.

7. Modelling the relation between field chlorophyll-a and eutrophication indicators

A regression model was developed using a spreadsheet that related chlorophyll-a to various eutrophication parameters. These parameters included total nitrogen, phosphates (due to the absence of total phosphorus in-situ data), dissolved oxygen, and salinity. The models tested included either one of the parameters versus chlorophyll-a or a ratio of two of the parameters versus values of chlorophyll-a. Eutrophication indicators in quarter 2, 2012 were empirically tested against interpolated chlorophyll-a data. These tested indicators included salinity, dissolved oxygen, total nitrogen, phosphates, and a ratio of any two of them. The model with the highest R-squared value was selected for relating it with the chlorophyll-a spectral model.

Cite this essay

Water Pollution. (2019, Dec 11). Retrieved from https://studymoose.com/water-pollution-essay

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