There is one World where people starve, literally, to death, where many countries are facing dramatic problems of water scarcity, a world in which the living conditions of millions of people are seriously threatened by climate changes. With this regard it is worth quoting a spokesperson of the World Meteorological Organization (WMO), Clare Nullis, who has said : ” Another month, another global temperature record has been set. So, surprise, surprise, April set new records for record high temperatures both on land and the ocean. This is the 12th straight month that we’ve seen temperatures records broken. What’s particularly concerning is the margin at which these records are being broken. They’re not being broken; they’re being smashed, and on a fairly consistent basis”. At the same time, on a parallel universe, there is another World where as much as 2 billion tons of food are wasted every year - equivalent to 50% of all food produced - according to a report published today by the Institution of Mechanical Engineers (IME). To produce food, we need water. In numbers, approximately 3.8 trillion cubic metres of water is used by humans annually with 70% being consumed by the global agriculture sector. The amount of water wasted globally in growing crops that never reach the consumer is estimated at 550bn cubic metres. These facts could look like a paradox, BUT they are not! To me, it seems quite clear that we are moving in the wrong direction, and we must stop immediately.
Whether it is called big data, machine learning, or data science – the many faces of the modern data analysis make headlines everywhere you look. The leap in technology in the past few decades allowed us to collect, filter and interpret huge bundles of data in a way that is unprecedented in the entire human history. Being the child of my time, I jumped that train during my PhD and want to share my excitement for the field with you.
The previous blog entry by my colleague Pau G was about PhD training courses. He covered the beautiful, tender-hearted, sentimental and touching part of PhD training. When I was a little boy, my parents used to send me to summer schools to get rid of me for a couple of weeks (I can’t blame them). I still remember how much fun I used to have; so many friends, activities and memories. Well, this Girona modelling course was exactly like that, and it wasn’t even summer.
April was definitively a busy month. As Luca posted, from the 18th to the 21st, we gathered together for the TreatRec second Advanced Training Course and then, from the 25th to the 29th, Pau J. and I attended a course at ICRA on modeling, control and decision support tools for sustainable wastewater treatment systems. Both courses aimed to train PhD students so I was surrounded by colleagues of my research field for over those 2 weeks. Socially wise, I enjoyed a lot meeting new people and, academically, I learnt a lot more from their respective research projects.
Last week I nearly broke into tears in the lab, and the reason was neither a failed experiment nor a reactor malfunctioning. No, the reason was that a colleague mentioned that he had read a paper that I contributed to some years ago. This unexpected strike of recognition worked its way through the rather thick skin I have grown during years of lab work and touched a tenderer spot inside me. Doing research is pretty rough, at least if you are working in a lab, most of us are quite content if not too many things go wrong and that in the end of the day you are still able to maintain the hope that you will get results enough to get a paper together. As encouragement we remind ourselves that we are part of a web of science and that great discoveries are the result of accumulated knowledge. Science recently presented statistics from Sci-Hub, the world’s largest pirate website for scholarly literature. Out of the nearly 30 million downloads from September 2015 to February 2016 the top one had little less than 8000 downloads and the number 10 had 1800 download. This illustrates the fact that scientists generally work in highly specialized fields but also gives an order of magnitude on how many (or few) people will read your publications. Therefore, that someone actually acnowledges to having read your paper and found it useful is quite amazing, at least for an early stage researcher.
If someone says :” I’m going down!”, you can be pretty confident he/she is in a bad mood, doing something which makes him/her sad! This is true. But, as usually happens in real life, going out from the “slang” field, words assume their real meaning. Last week the 5 PhD fellows of this TreatRec project went down, we went underground to visit some combined sewer overflow (CSO) sedimentation tank. What it is a sedimentation tank? I would not like to be rude, but visiting it was such an astonishing experience, which you should try once in your life, and so I am going to leave this door open… This means I will not explain what it is and how it works, but trust me, you do have “sediments” inside those tanks! A poetic statement from Sara Johansson (ESR3) works out better than pages of civil engineering books. While we were walking inside this tank, under the ground, wearing sexy safety clothes, and continuously measuring the H2S as a precaution, she (Sara) said:” It is like walking on the snow, more dark, stinky and maybe with some rat around you. But still it reminds of me walking on the snow in Sweden!”.
Self-directed education is in and free online courses from major universities and companies are leading the movement. So pack your lunch, sharpen your pencils and get ready to learn with my pick of the best self-education platforms on the web.
In previous entries I have explained what is resilience (towards wastewater treatment), and why is important. I have also explained that the measurement of resilience was going to be one of the main focus of my PhD. In this entry I will be talking about the main tool (or rather framework of tools) that will help me with the study: wastewater treatment (WWT) modelling.
The assessment of global uncertainty of predictions (i.e., pharmaceutical concentrations in rivers) in environmental modeling is a key issue and still an active research area. Throughout my whole academic and professional career I have been hearing that word (uncertainty) very often. In fact, I calculated it in almost every assignment I had to carry out during my Master and, luckily, the tools available by then were sufficient to this end.