Complex microbial communities are important in many environmental engineering processes. They degrade pollutants and help us recover resources. To optimize processes, we must understand how operational and environmental factors affect the function of microbial communities, and we must learn to control them. Methods to assess biodiversity and changes in community composition are helpful in this research.
When we study microbial communities, we typically sequence DNA from a sample and figure out the relative proportions of different microbes. Usually we find hundreds or thousands of different ones, but let's look at a more simple example. We have three samples and four detected species.
Sample A: 90%, 5%, 5%, 0% for species 1, 2, 3, and 4, respectively.
Sample B: 5%, 90%, 5%, 0%.
Sample C: 5%, 90%, 0%, 5%.
In samples A and B, we have detected species 1, 2, and 3, but not 4. Thus, we can say that sample A and B have the same microbial community composition. But, if we look closer we see that in A, species 1 make up 90% of the sample while in B, species 2 make up 90%. So samples A and B are perhaps not that similar after all.
If we compare samples B and C, we see that the two samples don't have the same species. Species 4 is lacking in B and species 3 is lacking in C. But, we also see that the species that make up 90% of sample is the same in both (species 2).
How should we compare the community composition in the samples? Are A and B more similar to each other than B and C, or vice versa?
It turns out there is a very nice systematic way of assessing the important of relative abundance on diversity and dissimilarity metrics. It is based on something called effective numbers or Hill numbers, which make it possible to tune the weight we give to relative abundance values. I think this Hill numbers should be used much more in microbial ecology.
I have developed a software (Python package) for calculation and visualization of diversity using the "Hill number" framework.
In the article Hill-based dissimilarity indices and null models for analysis of microbial community assebly published in the journal Microbiome, we describe how qdiv can be used to analyze microbial data sets and argue for the use of the "Hill number framework" in microbial ecology.
Waste streams such as wastewater and solid waste contain valuable resources such as energy, nutrients, and metals. How can we recover these resources from the waste streams?
Electrochemistry helps us convert chemical energy into electrical energy, and vice versa. Microorganisms can catalyze complex chemical processes. The combination of microorganisms and electrochemistry allow us to use complex chemical reactions in electrochemical systems. For example, we can extract electrical energy from the degradation of complex organic compounds present in wastewater or we can store electrical energy in chemical bonds by reducing carbon dioxide into organic chemicals.
Read more about possible applications of microbial electrochemistry in the water sector here.
You can also find more studies about microbial electrochemistry among the papers on my Google Scholar profile.
Aerobic granular sludge
In Sweden, we consume on average 140 liter water per person and day. After we use the water, it must be treated before we can discharge it into nature Can we improve existing treatment processes or develop new that consume less energy and chemicals, take up less space, and remove pollutants more efficiently?
Conventional wastewater treatment typically uses a process called activated sludge. Microorganisms grow as flocs suspended in a tank and degrade organic pollutants. Some process configurations can also be used for removal of nitrogen and phosphorous from the wastewater. After the activated sludge tank, the water and microorganisms flow into secondary settlers. The activated sludge (microbial flocs) settle to the bottom and the treated water is discharged. The flocs are then recirculated back to inlet of the activated sludge tank.
Aerobic granular sludge is a relatively new process that creates conditions that make the microorganisms grow in dense granules instead of flocs. The granules settle very quickle, which means we don't need a lot of space for large secondary settlers. The granules also contain microenvironments with different levels of oxygen. This is beneficial because it means different funcitonal groups of microorganisms can coexist, and we can accomplish complex processes such as nitrogen- and phosphorous removal in one single tank.
We have studied the startup of aerobic granular sludge in a series of laboratory scale studies. Granules are typically formed when we apply selection pressures such as high turbulence, feast-famine regimes, and short settling time. In one study, we asked if the same microbial community composition would develop in three reactors started up in parallel. You can read about the results here.
You can also find more studies about AGS among the papers on my Google Scholar profile.