Dear reader -
In a previous post I alluded to a database project that colleagues and I have recently undertaken to compile organic compound adsorption studies comprised of paired pilot or full-scale column datasets with RSSCT datasets using the same water-adsorbent-pollutant combinations.
The purposes of this data-heavy look at scaling organic micropollutant removal are to (1) reveal and describe our current best understanding of how to design rapid lab-bench-scale studies and interpret the results for projecting real-world system performance, and (2) enable a step-wise method of using RSSCT data, in particular for sentinel pollutants, to derive guidelines and decision support tools for practitioners to design and operate biochar water treatment systems in a variety of low-resource settings.
There are very few published studies for biochar with paired datasets from pilot and RSSCT experiments1. To build a more robust database we have expanded our review to encompass the GAC literature. In fact we’ve gone all the way back to some of the seminal GAC RSSCT work from the mid-1980s.
So far we’ve tabulated 105 pilot/PD-RSSCT data pairs and 69 pilot/CD-RSSCT data pairs. The studies have used water from diverse origins (surface waters, wastewater, groundwaters) containing background DOM at a range of concentrations. Sixty individual micropollutants are included in the database so far, from compound classes including PFAS, agrichemicals, pharmaceuticals and personal care products, volatile organic compounds (VOCs), disinfection by-products (DBPs), flame retardants and other manufacturing additives, and X-ray contrast media.
We’ll continue to pull in any new data pairs we find, but this is already a strong basis to work from.
My original idea was to take as dead-simple approach as possible and just compare RSSCT to pilot/full-scale results directly, and generate an empirical correction factor (with associated uncertainty range). In other words, adjust RSSCT breakthrough data for a given pollutant-water-adsorbent combination by a factor of X (plus-or-minus Y) to estimate full-scale breakthrough.
That turns out to work pretty well, actually. But it’s very “black box” - it doesn’t tell you anything about what’s going on, how the target compound’s adsorbability is affected by the concentration and character of the background DOM, etc.
So we decided to bump up one level of sophistication and develop a model that incorporates the physical-chemical properties of target compounds along with background water characteristics in a more nuanced approach to RSSCT scaling. The question is whether going to the trouble of incorporating various water chemistry factors gains any scaling precision advantage over the brute force dead-simple approach.
I’ll let you know how it turns out (work is still in progress). We’re planning to write this up as mini technical review for the peer-reviewed literature, but I promise to preview the main outcomes and actionable results for subscribers here first.
For now, if you’re interested in field-ready methods for pretreating feedstocks to enable the generation of “activated” biochar adsorbent, check out our recent paper in Chemosphere entitled “Pre-pyrolysis metal and base addition catalyzes pore development and improves organic micropollutant adsorption to pine biochar.” Use this link to get a free download (good until October 9, 2021).
Pretreating woody feedstocks with solutions of base (NaOH) and metal cations like sodium (Na), calcium (Ca), potassium (K), and magnesium (Mg) modifies biochar pore structures during pyrolysis in ways that can massively benefit adsorption properties! (Ref.)
See Kearns JP, Dickenson E, Knappe DRU, 2020. Enabling organic micropollutant removal from water by full-scale biochar and activated carbon adsorbers using predictions from bench-scale column data. Environmental Engineering Science, Vol. 37, Is. 6.
We have another study with biochar RSSCT/pilot data pairs for short-chain PFAS removal currently under review.