Putting on the LISST! Measuring Particles in Water Samples

Now that classes have finished and life is at a bit more regular pace, I thought I’d share a bit about some upcoming plans for my summer research in the Hudson River. The topic is an issue that I hope to work on throughout my PhD is better monitoring water quality in the Hudson.

Why Look at the Hudson River Estuary?

While many New Yorkers see the Hudson River as a moat separating them from upstate

HRE ldeo data puzzles
Outline of the HRE (photo credit: Lamont-Doherty: Data Puzzles)

New York or New Jersey, it’s actually a critical estuary. The Hudson River Estuary (HRE) serves as a nursery for diverse species of fish, a transition zone between saltwater and freshwater habitats for fish that are born in one and must live in another (i.e. anadromous fish, like the sturgeon), and a natural filter for water pollutants and nutrients. In fact, the Hudson River Estuary is acknowledged as being important for the biology found within the Atlantic Ocean. Not only does the HRE serve a vital ecological function, but also it provides a source of recreation for humans, from boating to fishing, swimming to jet skiing.

Unfortunately, the HRE is subject to heavy point-source pollutant fluxes, particularly following rainstorms when combined sewer systems (CSSs) intentionally discharge untreated human sewage and industrial waste directly into the waterways. This happens in order to avoid overloading wastewater treatment plants. Each year, more than 27 billion gallons of raw sewage and polluted stormwater discharges from over 450 CSS discharge points to the HRE (1). With increasing urban populations, the load placed on these antiquated sewer systems poses a considerable risk to environmental and human health. Already, a “wet” day, which is defined as within 3 days of only 1/4” rain, shows significantly elevated levels of fecal indicator bacteria, like Enterococcus(2). At the same time, these combined sewer overflows (CSOs) discharge high levels of particles and particle associated microbes(3), which is where my work begins!

What Is the LISST and How Does It Work?

The part of the project I’ll talk about today is particle size analyzing. Luckily, the lab I’m in has a Laser In-Situ Scattering Transmissiometry (LISST-100x) Particle Size Analyzer. That’s a lot of words to say that the LISST measures how many particles are in the water and what size they are.

Essentially, a laser diode shines light through the water and an annular ring detector measures the light scatter created by any particles in the water. Within the instrument, the angle is then converted to the size of LISST arrowsthe particle. The LISST-100x we use measures scattering at 32 different angles, which can all be converted to give us the size distribution of the particles. The size distribution is essentially how many particles are of a given size.

This week, I’m learning how to calibrate the LISST and measure water samples with it. To calibrate, we take a sample of highly filtered water and put it in the LISST chamber. This then makes sure that we know what the LISST measurement is when we know there should be no particles in the water. So, when we collect a real sample, we’ll subtract this background (called the Z-scatter) from the new data, to make sure we get a reliable measurement. An important thing to do in this process is let the water degas (air bubbles leave the water) and equilibrate in temperature (reach a uniform temperature). To do this, I filled a beaker with milliQ filtered water and let it sit with a loose lid in a 16°C (~61°F) walk-in fridge where I’ll do my LISST calibrations. We want to get rid of bubbles because light can be scattered by a bubble, making it seem as if there are really large particles in the water sample. Temperature instabilities also can make the results unreliable, as there can be increased movement of water in the sample. The fridge workspace requires some warmer clothes, since I’m in there for prolonged periods of time. Sweaters in the summertime!

I also want to test the LISST on real samples to see how everything works. Later, I’ll take it into the field and hopefully deploy it from a pontoon next to me while I kayak. Luckily, there are some professors at Lamont who have offered to help me set everything up for my first official in-situ (i.e. in the water) sampling adventure next week. For now, I want to make sure I can get the LISST happily running, so we don’t waste any time when getting ready to go out!









Two of my lab mates, Carol and Kayley, went down to Piermont Pier on Monday for their sampling and kindly brought me back a liter of Hudson River water. I also went out to the “Frog Pond” at Lamont to take a sample of water. Though it was a rainy day, it was brightened by seeing the frogs peaking out from under the lily pads. One thing I worry about for the LISST is that it might confuse the Hudson River’s high chromophoric/colored dissolved organic matter (CDOM) as particles, when it really is part of the natural organic matter in water. It’s mostly from tannin-stained (yes, tannins, like in wine!) waters, which are caused by decaying organic matter. CDOM especially absorbs short wavelength light (like blue to ultraviolet), which could interfere with the laser scattering and change particle counts.

Because I had to leave the lab before running the samples from the pier and pond, by the timixedlabeledme I got back the next day, the water had separated out a significant amount of the particles. Instead of homogenizing (well-mixing) them right away, I decided to measure particles in the particle-depleted top water from the beaker and then from when each sample was mixed together. Piermont Pier water shows the greatest change, from being dominated by lots of small particles and a sizable amount of large particles. Yet, when I homogenized the water, the distribution changed significantly, with small-medium sized particles becoming the most abundant, meaning that these had settled out so much that the initial Piermont Pier water sample did not reflect their abundance. The pond water was a much less dramatic shift, with homogenization increasing the abundance of large particles in the “tail end of the distribution,” which you can see in how the blue, resuspended water is higher than the initial pond water sample through the particle size range of 300 – 500 µm.


Something important to note about this graph is that we’re looking at abundance, which is a proportion of total particles, as opposed to the concentration, which would be an exact amount of particles in a given water sample. The LISST measures both, though a bit more data manipulation is necessary earlier. For this entry, I decided to stick with just abundance calculations, though I’ll be doing both in the near future for my summer research!

  1. HydroQual, Combined Sewer Overflows to New York Harbor Waters from New York City Watersheds for an Average Precipitation Year (JFK, 1988) Current Conditions (2003 Dry Weather Flow, 2003 Operations), September 29, 2004.
  2. Young, S., Juhl, A., & O’Mullan, G.D. (2013) Antibiotic-resistant bacteria in the Hudson River Estuary linked to wet weather sewage contamination. Journal of water and health, 11(2), 297-310.
  3. Suter, E., Juhl, A. R., & O’Mullan, G.D. (2011). Particle association of enterococcus and total bacteria in the lower Hudson River Estuary, USA. Journal of Water Resource and Protection, 3(10), 715.

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