GIS in the Assessment of Industrial Pollution and Its Effect on Vegetation (Paper)

8/12/2019 GIS in the Assessment of Industrial Pollution and Its Effect on Vegetation (Paper)

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GRP JORNAL OF ENVIRONMENTAL PLANNING & MANAGEMENT VOLUME 4 2011 67

GEOINFORMATION IN ASSESSING INDUSTRIAL POLLUTION: ITSEFFECT ON

VEGETATION: A CASE STUDY OF WARRI, DELTA STATE, NIGERIA

D. B. Alaigba1, Prof. D. O. Adefolalu

2and Prof O. O. Fabiyi

1

Department of Geographic Information Systems,1

Regional Centre for Training in Aerospace Surveys (RECTAS)Obafemi Awolowo University, Ile-Ife, NigeriaEmail:[email protected]

Department of Meteorology, 2Federal University of Technology, Akure

Abstract

Industrial pollution is a major problem the world over; as relates to environmental conservation and

sustainability of natural resources like soil, vegetation (food crops inclusive), water e.t.c. The Niger Deltaregion of Nigeria is the heart of crude oil exploration, where such activities as drilling, refining and

transportation of crude oil and petroleum products is carried out maximally. As a result of which oilpollution via oil waste disposal, oil incidents and vandallization of oil pipelines by bunkerers/hudlums is

released into the environment. This paper applied the use of remote sensing and Geographic informationSystems (GIS), in assessing the effect of such pollution on vegetation growth in Warri and environs over a

period of twenty five years. Landsat imagery of three time series (1987, 2002 and 2011) was used as inputfor NDVI analysis, soil samples were collected and the concentration of six heavy metals was tested for.Kriging interpolation was used to create surface models for two of the heavy metals tested. Statisticalanalysis using regression was applied to check the relationship between vegetation condition and

industrial location. The result revealed that there was a 30% loss of vegetation over the period andshowed high concentration of Zinc (Zn) in the soil which exceeded accepted standards. It also indicatedthat vegetation condition was enhanced with increasing distance from the Oil refinery as at 1987, but

subsequently the reverse was the case for 2002 and 2011 respectively, indicative of the fact that pollution

from oil industries affect both areas around it and areas far from its immediate environs.

Keywords: Industrial Pollution, Vegetation, GIS, Remote Sensing

Introduction:

Pollution is defined as the contamination of the earths environment with materials that interfere withhuman health, the quality of life or the natural functioning of the ecosystems (living organisms and their

surrounding environment). Ecosystems such as forests, wetlands, coral reefs, and rivers perform manyimportant functions for the earths environment. Ecosystems of such nature enhance water and air quality,

provide habitat for plants and animals, and provide food and medicines (Engelking, 2008).

Pollution reaches its most serious proportions in the densely settled urban-industrial centers of the more

developed countries (Kromm, 1973). Industries, continues to pollute the environment with impunity(Government of Pakistan, 2009). Pollution is caused by gas flaring, above ground pipeline leakage, oil

waste dumping and oil spills. Approximately 75% of gas produced is flared annually causing considerableecological and physical damage to other resources such as land/soil, water and vegetation. Madueke,(1983) stated that the oil aspect of the petroleum industry, including exploration, production, refinery,

transportation and marketing contributes largely towards increasing the concentration of pollutantsdischarged from stationary sources. Gas flares, which are often times situated close to villages, producesoot which is deposited on building roofs of neighboring villages. Whenever it rains, the soot is washedoff and the black ink-like water running from the roofs is believed to contain chemicals which adverselyaffect the fertility of the soil.
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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Udo and Oputa, (1984) stated that oil exerts adverse effects on plants indirectly by creating conditionsthat make the desired soil nitrogen unavailable to plants and expose the crops to toxic nutrients. He

concluded that depending on the degree of contamination, oil contaminated soil may be temporarilyunsuitable for farming until such time that oil in the soil is degraded to suitable level.

Vegetation in the Niger Delta consist of extensive mangrove forest, brackish swamp forest and

rainforests, the large expanses of mangrove forest are estimated to cover approximately 5,000 to 8,580kmsquare of land (Nwilo and Badejo, 2007). Mangrove is of great importance to both man and living

organisms in these ecosystems. The effect of oil spill on mangrove occurs through its impact of acidcontent on the soil which halts cellular respiration of mangrove plants and starves its root of vital oxygen.This paper aims at assessing the effect of industrial pollutant on Vegetation growth within Warri and its

immediate environs using Geoinformation techniques (Geographic Information Systems and RemoteSensing Inclusive) to determine how soil pollution has caused change in vegetation cover over the past 25

years.

Study Area

Warri and environs is a major oil producing area in Delta State of Nigeria which extends from Latitude 5 0

34

23.85N andLongitude 5

0

41

39.58 to Latitude 5

0

48

25.35N and Longitude 5

0

46

11.42E of theForcados River, on the western edge of the Niger Delta; it occupies an area of about 220 square km andhas a population of 303,417 (Nigerian Population Commission (NPC), 2006.) Warri is an industrial area

concerned with crude oil exploration and hence referred to as a major oil city in Delta state. The mainethnic groups found in Warri are predominantly Itsekiris, Urhobos and the Ijaws.

Warri sits on the bank of theNiger Delta and has a modern seaport which serves as the cargo transit pointbetween theNiger River and the Atlantic Ocean for import and export. Warri is surrounded by a tropical

rain forest and swamp. The region experiences high rainfall and high humidity for most part of the year.The climate is equatorial and is marked by two distinct seasons, which are dry and rainy seasons. The Dryseason lasts from about November to April and is significantly marked by the cool "harmarttan" dusty

haze from the north-east winds. The Rainy season spans May to October with a brief dry spell in August.But it frequently rains even in the Dry season. The area is characterized by tropical equatorial climate.

There are high temperatures of 36C and 37C. The natural vegetation is of rainforest with swamp forest insome areas. The forest is rich in timber trees, palm trees, as well as fruit trees. The economic base of thecity lies in the presence of arefinery and otheroil and gas companies.
http://en.wikipedia.org/wiki/Niger_Deltahttp://en.wikipedia.org/wiki/Niger_Riverhttp://en.wikipedia.org/wiki/Refineryhttp://en.wikipedia.org/wiki/Petroleumhttp://en.wikipedia.org/wiki/Petroleumhttp://en.wikipedia.org/wiki/Refineryhttp://en.wikipedia.org/wiki/Niger_Riverhttp://en.wikipedia.org/wiki/Niger_Delta


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Figure 1.1: DELTA STATE SHOWING WARRI AREA

Figure 1.2: STUDY AREA (Warri and Environ)


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MATERIALS AND METHODS

Data and Materials

This research involved the integration of both primary and secondary dataset.

The Primary data set used for this work includes;

GPS locations of industries in the area Soil Pollution data (Obtained from soil samples collected in the field using random sampling)

The secondary data used for this work include;

Landsat Imagery (28.5 meters resolution) of three time series (1987, 2002 and 2011)The software used for this work includes; ArcGIS (for database creation, data integration, Spatialanalysis (Krigging interpolation) and data visualization), and ILWIS ( for NDVI analysis).

Table 2.1: A Table Showing Data Sources

S/N DATA DESCRIPTION SOURCE

1 Landsat ETM Image Date: 1987, 2002 & 2011Resolution: 28.5meters

Global Land Cover Facility(GLCF)

6. Soil Pollutant Data 2012 Field Survey

7. GPS Point Data Industrial locations Field Survey

Normalized Difference Vegetative Index (NDVI)

The Normalized Difference Vegetative Index (NDVI), is a calculation based on several bands, of thephotosynthetic output (amount of green stuff) in a pixel in a satellite image, it measures in effect theamount of green vegetation in an area. NDVI calculation is based on the principle that actively growinggreen plants strongly absorb radiation in the visible region of the spectrum (Photosynthetically ActiveRadiation (PAR)) while strongly radiating in the Near Infrared region. The concept of vegetative spectralsignatures (patterns) is based on this principle.

NDVI is calculated from the Red and Near Infra red reflectance as shown from this formula;

NDVI= (NIR-RED) / (NIR+RED) (1)

Its value is always between -1 and +1, NDVI decreases as leaves come under stress become diseased or

die. Bare soil values are close to zero while water bodies have negative values. In the course of thisresearch, NDVI was used to determine the presence and condition of vegetation in the area over the

period of twenty five years using the Landsat imagery for the same area for three time periods, 1987,2002 and 2011 respectively. To do this band 3 (Red band) and band 4 (NIR band) of the Landsat imagerywas used in Integrated Land Water Information System (ILWIS) Software.

SOIL SAMPLING

Surface soil sampling was carried out randomly within the study area at (0-15cm) depth; these sampleswere collected with a hand auger from points generated randomly within the study area. Twenty samples


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were collected in total both close to the industrial zone and at relative distance away from the industrialzone (for the purpose of control)

The collected samples were then taken to an environmental laboratory for testing of the six contaminants.

Figure 3.4: SOIL SAMPLE POINTS (Random Sampling

)

This research ascertained the level of six heavy metals present in the soil of Warri area of Delta state.

These contaminants are mostly known for their strong influence on plant growth, they include; Arsenic,Cadmium, Zinc, Copper, Nickel and Lead.

Kriging Interpolation

A grid representation of a surface is considered to be a functional surface because for any given x,ylocation it stores a single z value as opposed to multiple z values functional surfaces are continuous 2.5

dimensional surfaces because an x,y location has one and only one z value regardless of the direction

from which the x,y point is approached and are not considered as 3 dimensional surfaces. In order tocreate a surface grid in which each cell contains an attribute value that represents a change in z value,interpolation technique is employed. Interpolation is a technique used to predict the values of cells atlocations that have no sampled point. Interpolation is based on the principle of spatial autocorrelation, or

spatial dependence, which measures the degree of relationship/dependence between near and distantobjects. Spatial autocorrelation determines if values are interrelated, if they are it determines if there is aspatial pattern. As a result of the fact that in the real world it is difficult to get exhaustive values of data at

every desired point because of practical constraints, interpolation is important and fundamental tographing, analyzing, and understanding of 2D data. In this research Kriging technique was used to


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generate values for un-sampled portions of the study area, from ten sampled points for soil pollutionparameters applying krigging tool in ArcGIS 9.3.

3.0 Result and Discussions

From the NDVI histogram for year 1987, in figure 3.2, vegetation yields higher values ranging from 0.1to 0.3, while built up area, rock and bare soil have values ranging from 0 to 0.1 and water body has thelowest values ranging in the negative. Vegetation in Warri area has over 12,000 pixels of about 28.5m

each assigned to it as of 1987. This is indicative of the fact that the area is rich in vegetation of mainlyuntouched mangrove forest and less of shrub.

Figure 3.1 1987 NDVI for Warri and Environs


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Figure 3.2 NDVI Histogram (1987)


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Figure 3.3 Year 2002 (NDVI) For Warri and Environs


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The vegetated area has reduced by about 50% over a period of 15 years as shown in the NDVI histogramfor the year 2002 in figure 3.4 above, vegetation yields higher values ranging from -0.1 to 0.12, while

built up area, rock and bare soil have values ranging from -0.28 to -0.15 and water body has the lowest

values ranging from -0.44 to -0.28. Vegetation in Warri area has over 6,000 pixels of about 28.5m eachassigned to it as of 2002. This is indicative of the fact that most of the untouched forest has given way toyounger or secondary vegetation like shrubs.

Also the NDVI histogram for the year 2011 in figure 3.6 below, vegetation values range from 0.04 to0.28, while built up area, rock and bare soil have values ranging from -0.10 to 0.04 and water body has

the lowest values ranging from -0.31 to -0.10 as shown in the map. Vegetation in Warri area has over8,000 pixels of about 28.5m each assigned to it as of 2011. This is a little improvement from the year

2002, but still overall vegetation has reduced by about 30% over the last twenty five years.


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Figure 3.5 Year 2011 NDVI For Warri and Environs


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Soil Pollutants

of the six soil pollutants tested for, Nickel, Arsenic, Cadmium and Lead were of trace amount within the

study area as indicated in the table above and were all about the same amount as the past five years, thesefour heavy metals were all within the accepted standard of >50ppm stipulated by the Department of

Petroleum Resources (DPR). Copper (Cu) was found to have values as high as 9.54ppm, even though itwas still within the standard limit. Zinc (Zn) was found to exceed the standard limit of 140ppm. Of the tensamples collected, Zinc (Zn) exceeded the standard limits in two locations having values as high as

289ppm. Highest levels of Zinc (Zn) were observed at sample points collected close to the river channel,and at locations close to the industries than at locations father apart. The amount of Zinc within the soil

has increased over the past five years and is very harmful to the growth and development of plants within

the study area.

From the map above, it can be seen that the most impacted settlements are, Ekpan, Ubeji, Egbokodo,Jeddo and Effurun. Hence planted crops in these settlements will have trouble thriving. Zinc is essential

for many plant functions, among which are production of auxins, activation of enzymes in proteinsynthesis, regulation and consumptions of sugars, starch formation and proper root development,formation of chlorophyll and carbohydrate, hence only a limited number of plant species can survive onzinc rich soil.


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Table 3.1 Heavy Metal Content in Soil (2012) (Laboratory Result)

S/N Copper

(Cu)

ppm

Zinc (Zn)

ppm

Nickel (Ni)

ppm

Lead (Pb)

ppm

Arsenic (As)

ppm

Cadmium

(Cd) ppm

1 4.1 4


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Figure 3.8 Year 2012 Level of Zinc (Zn) Soil Pollutant in Warri and Environs

Statistical Analysis

This research involved the use of regression analysis to test the relationship of vegetation condition and

its distance from industrial location. The variables used for this analysis was the indexed value of tenNDVI cells each (for three times series) and the distance of those cells from the Warri Refinery and

Petrochemical Company. The distance was represented as (y) on the vertical axis, which is the variable

that does not change through time. While (x), was the indexed value of a cell in the NDVI, also regardedas the dependent variable.


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Table 3.2 Data for Statistical Analysis

S/N Distance (m):

y

NDVI Cell Value

(1987): x

NDVI Cell Value

(2002): x1

NDVI Cell Value

(2011): x2

1 3745 0.26 0.02 0.15

24995 0.17 -0.24 -0.12

3 5557 0.22 -0.09 -0.15

4 6042 0.15 -0.16 -0.13

5 8563 0.22 0 0.1

6 10323 0.11 -0.07 0.11

7 10194 0.18 -0.01 -0.01

8 8687 0.25 0.08 0.32

9 10187 0.24 -0.01 0.23

10 2609.97 0.21 -0.24 0.01

Table 3.3 Regression Analysis

ANOVA

df SS MS F (F Cal)

Significance F

(F tab)

Regression 3 51873424.59 17291141.53 4.79813273 0.049142053

Residual 6 21622338.33 3603723.055

Total 9 73495762.92

Table 3.4 Probability Value of X for three Years

Coefficients Standard Error t Stat P-value

Intercept 16883.60421 3363.744905 5.019287933 0.00240576

X (1987) -42315.8078 15548.28806 -2.721573438 0.03457048

X1 (2002) 20384.21631 8718.605226 2.338013453 0.05800132

X2 (2011) 3526.151619 6022.912132 0.585456261 0.57957855

From the statistical analysis carried out, the regression analysis in table 3.3 showed that: F which is Fcalculated is greater than Significance F, which is F Tabulated. That is F Cal > F Tab. This confirmed

that the alternative Hypothesis (H1) is true; hence there is a significant relationship between healthcondition of vegetation and its distance from industrial location. The vegetation condition is enhancedwith increasing distance from industrial location. The reverse is the case for the other two years as thevegetation condition deteriorates with increasing distance from industrial location, for year 2002 and 2011

as shown in table 3.4.

Conclusion and Recommendation


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GIS and Remote Sensing proved a remarkable method in assessing soil pollution via industrial waste andits influence on vegetation change over time in Warri and environs. The regression analysis conducted to

show the effect of industrial pollution on vegetation in the area, confirmed that there is a significantrelationship between health condition of vegetation and its distance from industrial location. Thevegetation condition deteriorates with increasing distance from industrial location, for year 2002 and 2011as shown in table 3.4, indicative of the fact that, Industries within the area may have far reaching impacts

on the environment, and that settlements that are not within the immediate environment of the oil industrymay also be negatively impacted.

As a result of the fact that heavy metals are non-biodegradable, action must be taken to mitigate theirdirect bio availability and transfer to the food chain. Remediation practices through stabilization can beused to mitigate the negative effect of zinc in soil. Chemical stabilization can also be used to mitigate thenegative effect of Zn on the environment. This method involves the application of materials that canlower the mobility of Zn, as zinc in soluble fraction create potential risk to the environment.

References

Engelking Paul, 2008. "Pollution." Microsoft Student 2008 [DVD]. Redmond, WA: MicrosoftCorporation, 2007.

Government of Pakistan (2009), Economic Survey of Pakistan, Finance Division, Economic Division

Wing, IslamabadKromm, D. E. (1973). Response to Air Pollution in Ljubljana, Yugoslavia, Annals of the Association of

American Geographers, 63(2), pp. 208-217.

Liu D. and Y. Wang (2002). Effect of Cu and As on germination and seedling growth of crops. Chin. J.Appl. Ecol., 13: 179-182.

Madueke, C.J. (1983), Environmental protection: Prospects for implementation in Nigerian PetroleumIndustry. Napector.

Nwilo, P. C and O. T. Badejo (2007).Impacts and Management of Oil Spill Pollution Along the NigerianCoastal AreasInternational Federation of Surveyors.

Udo E.J and C.O. Oputa (1984). Some case studies on the effects of crude oil pollution of soil on plantgrowth.
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GIS in the Assessment of Industrial Pollution and Its Effect on Vegetation (Paper)