Watershed (Research Paper Sample)

Student’s Full Name
Professor’s Name
Subject
Date
Investigate whether the conductivity has increased in the Hudson River Watershed over the decade
Abstract
The Salinization of water is a common occurrence in the world. More and more regions are involved in water salinization around the world. This Natural phenomenon is more apparent in the coastal regions because the growing lack of water, which induces the intrusion of seawater into internal freshwater reservoirs, turns it into brackish waters that are virtually unusable for agriculture, human consumption and industrial use. High salinity concentrations in water and soil cause corrosion of equipment and structures when it comes into contacts with them, such as gates, roads and bridges. Lousy health or death of natural plants, leading to a decrease in ecology due to the domination of salt-resistant organisms, eventually altering ecological systems. Other than this, high salinity lead to high conductivity in water. High conductivity leads to high acidity and low levels of oxygen along with making the water murky. The purpose of this paper was to see and investigate that the conductivity in the Hudson River Watershed has increased over time. Regression analysis was used to confirm this statement, and the trends were observed. Time and conductivity were taken as independent and dependent variables. After gathering the data and following it, it was statistically investigated whether the conductivity has increased over time. The paper includes an introduction which consists of the background, methodology, results and then it is summarized with the conclusion.
Introduction
Human beings are steadily increasing the abundance of salt in freshwater worldwide, contributing to significant impacts on the environment, the atmosphere and the ecosystem. The aim of this paper is to investigate whether over the years, the conductivity in the Hudson River has increased or decreased. To investigate this, different statistics are used that will help us establish our point. The additional questions that this paper will answer will include; how the conductivity of water decreased and then eventually increased over the years if the answer is positive and what were the problems that caused the conductivity of the water to increase?
Conductivity is a measurement of the potential of the water to move through the electrical current. This skill is linked directly to the density of ions in the water. These particles are generated from salts which are dissolvable and items which are inorganic, for example, soluble bases, chlorides, sulfides and carbonate mixes. Blends that are separated into components are commonly regarded electrolytes. The water conductivity increases as there are more particles in the water, while the conductivity decreases as there are less particles in the water. Refined or deionized water can be filled as a separator due to its poor conductivity content. A reason behind increase ions in water is due to salinity (Argüelles, Kefford and Schäfer). A massive contributor to conductivity is salinity. It is the principal component of all the salts that are dissolved in water. In that capacity, saltiness is a massive supporter of conductivity.
The origins of much of the residual pollutants of the Hudson River are spread across the thirteen thousand, three hundred and ninety (13,390) square-mile watershed; the land on which water runs into the Hudson River. This non-point source waste includes oil and grease from parking lots and roads, excess fertilizers and chemicals from fields and salts from land that is left barren by development and other environmental problems (The Hudson River Watershed). A significant source of nitrogen and phosphorus inputs into surface water sources is the treatment of wastewater in the Hudson River system (Phillips and Hanchar). Public-supply withdrawals are treated with wastewater which influences the concentration and transport of nutrients within the basin.
Methodology
The data was gathered to understand the trends of water conductivity in the Hudson River and then analyzed using linear regression. Linear regression is a simple method of predictive analysis that is widely used. The general concept of regression is to analyze two things: can a series of variables do a decent analysis to determine an overall result? And what factors, in specific, are the strongest determinants of the dependent variables. A Graph is made on the basis of the data formed from HRECOS. The two variables I used for the analysis were time and conductivity.
The data is gathered from the Hudson River Environmental Condition Observing Data (HRECOS). It took almost a week to collect all the data. The Hudson River Environmental Conditions Observation System (HRECOS) is a comprehensive atmospheric tracking system run and maintained by a group of federal, professional, and private organizations with a common purpose in elevated- frequency monitoring in the Hudson River watershed. HRECOS control points are strategically spread throughout and all along the Hudson River and are fitted with detectors that constantly capture a sequence of quality of water and climatic variables every 15 minutes. External satellite data transfer authentic information to the public for free streaming and sharing using the easy-to-access app at hrecos.org (Hudson River Environmental Conditions Observing System). This will help in analyzing whether the conductivity has increased or not.
The geographical location where the research is conducted is upstream of where brackish water is found in the Hudson River estuary. The major brackish water ecosystems in the globe are estuaries. The research is conducted in that area because the brack water has more salinity. This will help in coming to a conclusion.
Result
After gathering the data, it was analyzed using regression analysis from HRECOS. For the measurement of conductivity, activity was observed from the year 2008 to the year 2016.
The independent variable used is time. The independent variable is just as it implies as. It is a variable that stands out and is not affected by the other variables that are being calculated in the analysis. The dependent variable in the graph is conductance. The element that relies on the other variables that are being evaluated.
SUMMARY OUTPUT

















Regression Statistics








Multiple R

0.526602








R Square

0.277309








Adjusted R Square

0.277289








Standard Error

0.022152








Observations

35135

















ANOVA









 

df

SS

MS

F

Significance F




Regression

1

6.615548

6.615548

13481.16

0.001




Residual

35133

17.24065

0.000491






Total

35134

23.8562

 

 

 













 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-5.27054

0.0476

-110.727

0.000

-5.36383

-5.17724

-5.36383

-5.17724

42370

0.00013

1.12E-06

116.1084

0.001

0.000128

0.000132

0.000128

0.000132

From the regression analysis, it was observed that there was a shift in con...