Online College Courses for Credit

4 Tutorials that teach Graphs in Environmental Science
Take your pick:
Graphs in Environmental Science

Graphs in Environmental Science

Author: Jensen Morgan

This lesson demonstrates the role of graphical representations in environmental science.

See More
Fast, Free College Credit

Developing Effective Teams

Let's Ride
*No strings attached. This college course is 100% free and is worth 1 semester credit.

29 Sophia partners guarantee credit transfer.

310 Institutions have accepted or given pre-approval for credit transfer.

* The American Council on Education's College Credit Recommendation Service (ACE Credit®) has evaluated and recommended college credit for 27 of Sophia’s online courses. Many different colleges and universities consider ACE CREDIT recommendations in determining the applicability to their course and degree programs.


Source: Earth PD Edinburgh PD Ice Core CC California PM CC Pie Chart CC Chart Tool CC Deforestation, PD Ocean Trash CC

Video Transcription

Download PDF

Hi, I'm Jensen Morgan. We're going to talk about some great concepts in environmental science. Today's topic is graphs in environmental science. So let's get started.

We're going to talk about graphs used in environmental science, how graphs can be manipulated to present false or misleading information, and discuss examples of how environmental photos and their purpose. We're going to talk about four different examples of graphs that are used in environmental science, though there are more that we will not discuss. I am going to pair each graph with a verbal equivalent of what the graph is communicating. For example, this first chart shows that Edinburgh, Scotland's capital, has a mean peak maximum temperature of about 19 degrees Celsius in the month of July, and a low mean minimum temperature of about zero degrees Celsius in the month of February.

Now let's take a look at a line and bar chart combination to see the visual representation of this. The title is Edinburgh Climate. As you can see at the bottom, the legend explains that the red line graph is for mean daily maximum temperature, and the blue one is mean daily minimum temperature. The x-axis here indicates time with the months of the year, while the y-axis indicates temperature in Celsius on the left. This graph also shows precipitation in Edinburgh year-round in millimeters on the right, making for a second y-axis.

This next graph is of an ice core sample taken from Vostok, Antarctica. It shows multiple things, one of which is that the Earth has had multiple peaks in temperature and CO2 concentrations in the atmosphere over the last 400,000 years. Looking at the graph, you can see that the title of Vostok Antarctic Ice Core is right there.

There is no legend in this line chart, but the x-axis shows time in 1,000 years, starting at the making of the graph and going back 400,000 years. There are 3 y axes showing three separate units-- dust in parts per million, carbon dioxide concentration in parts per million by volume, and change in temperature in degrees Celsius. The graph shows a cycle of rises and falls in temperature, CO2, and dust over the last 400,000 years.

Now we're going to flip things the other way. We're going to look at a chart, and then look at a written representation of it. The title here of this scatter plot explains that this chart is about particulate matter pollution of a certain size recorded as a daily mean concentration for Kern County, California.

The x-axis shows time from the year 2001 to 2010, with a data point for every day of the year. The y-axis shows the particulate matter concentration measured at 2.5 micrograms per cubic meter. You can see that the majority of data points fall below 20, but spike seasonally. A written version would look something like this-- Kern County, California's daily mean particulate matter 2.5 micrograms per cubic meter from 2001 to 2010 had the vast majority of days at or below 20. There is a seasonal variation in the middle of the year where a spike occurs.

Let's do one more like this. The title here is Annual world greenhouse gas emissions in 2005 by sector. This pie chart doesn't have any axes or legend, but its subdivisions are separated by sector. As you can see, certain sections take the majority of the pie, while a few others make up the last small bit.

A textual version might look something like, "In 2005, the majority of greenhouse gas emissions came from the electricity and heat sector, followed closely by Industry and Transportation. A few other sectors produced as well, but were only minor emitters.

We're going to look at two different graph types presenting the same information. This first one is a bar graph of a fictional region. The title is up here.

The legend here indicates that the bar chart and its color represents rainfall. The x-axis represents units of time in years from 2012 to 2014. The y-axis represents rainfall in millimeters. From this graph it can be concluded that the year with the highest amount of precipitation was 2014.

This is a different chart called an area chart using the same data. Once again, here's the title indicating that this is a graph about rainfall in a fictional region. The legend is the same as the last. The x-axis is the same, and the y-axis is also the same.

However, the data points are represented as shaded-in sections of the graph underneath them. This makes the graph look different, even though it is the same information. Despite appearances, the same conclusions can be drawn. 2014 had the highest precipitation in millimeters.

Now we're going to discuss how graphs can be misleading. Common ways graphs mislead viewers are, using a manipulated scale that doesn't begin at zero, using a misrepresentative title, using uneven increments on an axis, creating a gap in the scale, and/or using a 3D or confusing design to mislead viewers. An example of something like this would be this graph here. It is a made-up graph about a fictional corporation's sulfur dioxide emissions over time.

The graph appears to indicate that the company's emissions are going down. However, there's a gap in the scale. The graph only shows data from the years 2010, 2012, 2014, and 2015. The years 2011 and 2013 are entirely missing. This can mislead the viewer's conclusion.

Let's look at what this graph is like with those two years included. Wow, see? 2011 was the same as 2010, and 2013 was higher than all the rest. This graph provides a more accurate picture of what the fictional corporation's sulfur dioxide emissions look like from year to year.

Photography can be used to convey information and generate emotions. Photography of environmental science issues, like this one here of trash collecting in a large jumble in the middle of the Pacific Ocean, can convey information on environmental science topics like oceanic waste. However, it is not a purely scientific endeavor necessarily.

This photo may also have been taken with the purpose of generating feelings of sadness, guilt, or ecological stewardship. The goal of the photo may have been to encourage better ecological behavior such as recycling or regulation on ocean waste dumping. Always remember what platform you are consuming in scientific information from and what biases might be incorporated, depending on the source, be it a scientific journal, an environmentalist magazine, a political source, or popular media.

Now let's have a recap. We discussed graphs used in environmental science, how graphs can be designed to be misleading, and how environmental photos can convey both information and emotion. Well, that's all for this tutorial. I look forward to next time. Bye.