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Pollutants can be such a headache!

By: Daniel Trevino

If you get hit by a rock what would be the first thing that would come to your mind? “Who threw this rock at me” would probably be the first thing to come to mind.  Well what if you cannot find where the rock came from? Then, that would raise even bigger aching questions. Now this concept works rather well when speaking about the way industrial pollutants can come out of seemingly nowhere and cause major damage to environmental systems. Furthermore, if you cannot find their sources then what does this mean for environmental sustainability? Now what is interesting is that there is a group that researched a method in identifying some of these pollutants. According to Sun et al. they have developed a modeling method known as, “deep convolutional neural network (DCNN)” that can track down these pollutants.



Figure 1: This is the DCNN model and it takes a chemical, runs it through a set database, and makes 2D models in order to identify the chemical. The accuracy and amount of chemicals are also listed.

Figure 1: This is the DCNN model and it takes a chemical, runs it through a set database, and makes 2D models in order to identify the chemical. The accuracy and amount of chemicals are also listed.


In addition, this model can track down harmful pollutants with an accuracy of 95% (Sun et al. 2020). The identification of these harmful industrial chemicals is can prove to be essential pollution prevention and chemical cleanups because it can help identify where they are coming from and where they are in the environment. The costs of this method is that it has not been tested with newer industrial chemicals and it is focused around two specific types of pollutants known as persistent organic pollutants and persistent, bio-accumulative, and toxic pollutants. Nonetheless, this method is a step in achieving a more environmentally sustainable world through the identification of those pesky pollutants!

References:

Sun, X., Muir, D. C. G., Zeng, E. Y., & Zhang, X. 2020. Identification of potential pbt/pop-like chemicals by a deep learning approach based on 2d structural features. Environ. Sci. and Technol. 8221–8231.


Comments

  1. Odalys EspinozaNovember 19, 2020 at 9:52 PM

    Loved the intro and how you connected it with a real-life scenario! It amazes me how technology keeps evolving giving us opportunities like these to detect harmful pollutants from a device. I wonder if we'll ever be able to create a device that can detect all sorts of pollutants and how that would affect our environment.

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