“Global Carbon Footprint”

An infographic is a popular form of content communication that can simplify a complicated subject or turn an otherwise boring subject into a captivating, learning and inspirational experience. Ideally, an infographic should be visually engaging and contain a subject matter with organized or processed data that is appealing to your target audience.

Today, we prefer to make reports and presentations using infographics. The infographic speaks thousands of words and provides an insight more than just the data. In creating infographics, we often “crunch” the data and present associations between various data elements. This analyses  with right visualization leads to astonishing interpretations. Unfortunately, not many are skilled or experienced in creating meaningful and captivating infographics.

I asked my Professor Friend for an example. He was completing a report on water quality data analyses on river Godavari in Maharashtra, India.

“Well, Dr Modak. The Maharashtra Pollution Control Board gave me water quality data on 18 stations over 10 parameters sampled every month over 8 years. The interest was to know whether the water quality in Godavari was improving between 2012 and 2013. So, I thought of creating an infographic. The first step I had to do was to crunch the 10 parameter water quality data into a single parameter. I selected  for this purpose a Water Quality Index (WQI). I looked at many options and chose a structure recommended by the Central Pollution Control Board. I generated a time series of WQI at all the 18 stations for 2012-2013”. Professor showed me an MS Excel sheet where all the required number crunching was done.

He then lit his cigar.

“The main interest was to know whether the water quality was improving or worsening. So, I realized that I had to do some quantitative trend analyses. I applied Kendal-Tau test to assess for each station the trend in WQI which gave me the direction of the trend (upward or downward) and its significance. So, the time series data on WQI was converted into a single number at each station viz. trend”

“Clever – Professor. So, you essentially reduced 24 x 10 data points into just one number for each station” I exclaimed

“Well, that was not enough. I had to bring in visualization now to help understand the changes” Professor projected a slide that depicted river Godavari with locations of monitoring stations and the results of the trend. So, this was the infographic. It not only communicated the trend but opened discussions on why the trend reversed in the adjoining stations and possible field investigations that were needed. Showing on the map the key points of wastewater discharges would be have been very useful but MPCB did not have such data”


The example of infographics for water quality in Godavari showed how techniques of data crunching and spatial visualization were used for effective communication and actioning.

Let us now look at the power of data association in creating infographics.

Figure below shows a construct of time series of data collected at two automatic air quality monitoring stations (BT4 and HR1) for parameters PM10 and NOx .  The time series can be viewed to see the outliers (values crossing four times of standard deviation per Dixon’s test) as well as spot values crossing the applicable ambient air quality standard. But it is perhaps more interesting to find instances when both PM10 and NOx   are simultaneously exceeding over the standard.   These instances (red dots) tell us more about the associativity of PM10 and NOx emissions for better source diagnosis.

Similar extensions could be made. The time series of stack emission data can be plotted and “associated” with time series of ambient air quality data. Hourly observations on wind directions could be then used to estimate the “stack influence” and a time series plot of stack influence could be generated by setting an “influence function”. A time series of stack influences can be used to assess the relevance of ambient air quality monitoring station (i.e. siting) and importantly take actions for controlling the stack emissions. See Figure below.

The data association approach can be effectively used to communicate “causation”, especially to the non-scientific community by preparing striking visualizations. I remember Professor showing me a map where concentrations of PM10 were plotted along with information on sale of inhalers at the shops of chemists. We could see here interesting association between PM10 concentrations and the sale of inhalers – wherever PM10 was found to be high, there was high sale of the inhalers. See Figure below.


Sometimes we run into a situation where the management is interested to get a top view of the “situation” and then go into the details for actioning. In a typical Environmental, Health and Safety (EHS) audit, the Team after the plant visit writes a report, generally in the form of table that states the Non-Compliances (NC). While such tables are needed, an infographic that presents a quick snap shot in the form of a summary is always useful as the first slide of the presentation. Figure below shows application of a Fish-bone diagram where NCs are shown in perspectives, in a manner easy to grasp and importantly help prioritize and take actions.  The strategy in creating such infographic is to capture the key perspectives of the “problem” (or the eye of the fish) and then overlay on the fish-bones the “highs or lows” or “good or bad” performance against the benchmark or standard.



Sometimes, we need to prepare “popular” and easy to understand infographics, especially when conducting awareness events in schools and for citizens. These infographics follow the principle of cascading information where issues are flagged in a step-wise manner, material/energy flows are shown and outcomes or impacts on health for example are stated with damage in economic terms. Here icons, colors and data points in large fonts are used to get the needed focus or attention of the eye. Two examples of such infographic are shown below.


Finally, there are challenges when we create infographics for concept communication. Such infographic is generally hard to make. Couple of years ago, I created an infographic to communicate the concepts of Project EIA, Regional EIA and Strategic EIA. See Figure below

The crux of this infographic are the three baloons shown for each category of project, Category A for most sensitive, B – moderately sensitive and C – Not sensitive. The infographic communicates EIA at project level, for plans and for policies using the impact typology of direct, indirect, cumulative and induced impacts. The most important communication was to bring out limitations of project EIAs for area wide projects and need to use Environmental & Social Management Framework (ESMF)

“Dr Modak, creating infographic is more of an art or creativity than just data and science. It is a stimulating exercise. Our environmental students should learn this art of science” Professor was ordering some coffee.  “Data crunching, data association, structuring infographic based on perspectives and cascading information in the form of cause-effect or input-outcome are some illustrations. But there are numerous other possibilities, especially in the complex and multidisciplinary subject of environment”

I couldn’t disagree with Professor.  I wish we did with students, group sessions on how to create the infographics from seemingly drab data into something exciting and innovative– These teaching sessions will certainly lead to some out of the box thinking – I thought

“Should we  run a competition on infographics in environmental management Professor” I suggested

“Oh, clever idea. But call the competition as Enfographics” Professor said while finishing his coffee. I realized that he just coined a new term!!

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Cover image sourced from https://in.pinterest.com/pin/492651646708569178/


3 thoughts on “Enfographics

  1. Very relevant and nice piece Sir.
    Not only students, the professionals in the field too need a good understanding of infographics or as you say Enfographics. Most reports with data analysis are so drab to read through even for us from the field, that it would be impossible for those outside to even get a meaningful understanding.


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