Indicator for Algal Bloom Detection

A surplus of phosphorus in freshwater systems can cause algae, such as phytoplankton and cyanobacteria, to grow out of control. Such algal blooms can threaten drinking water supplies. Some algal species, for example, release unpleasant odors or toxins. An effective warning system for impending blooms would be useful: If one could predict when an algal bloom is likely to develop, it could be managed by removing or killing the algae before they become a problem.

Alkaline phosphatase is an enzyme that is ubiquitous in freshwater systems. It helps to release bioavailable phosphorus from phosphorylated species. Rising levels of the enzyme could be used to predict phosphorus-related algal blooms. However, existing detection methods for alkaline phosphatase are not very sensitive or specific.

Jingjing Deng, East China Normal University and Institute of Eco-Chongming, both Shanghai, China, have used the reaction catalyzed by alkaline phosphatase to induce both fluorescence and visible color changes in a water sample. The team combined copper ions with guanosine-5-monophosphate (GMP), forming infinite coordination polymer (ICP) nanoparticles that act as a host. Then they incorporated 1,1,2,2-tetra(4-carboxylphenyl)ethylene (H₄TCPE) and sulforhodamine 101 (SR101) within the nanoparticles as guests. H₄TCPE has aggregation-induced emission (AIE) properties and SR101 shows aggregation-caused quenching (ACQ).

The researchers obtained a deep blue solution with a bluish-purple fluorescence under UV light. In the presence of alkaline phosphatase, GMP was dephosphorylated and the solution shifted to a pinkish hue, with a strong red fluorescence under UV light. This is due to the change in the aggregation state of the guest compounds. The researchers tested the indicator with water from 13 river locations that had limited bioavailable phosphorus, calculating red-to-blue fluorescence ratios with a smartphone color-scanning app. They found that the portable digital method reliably detected alkaline phosphatase.

The researchers also grew toxin-producing cyanobacteria in the lab, feeding them complex phosphorus-containing compounds, and measured the changes in alkaline phosphatase activity. On the third day, a large increase in enzyme activity was detected with both fluorescence and visible color changes. A few days later, the algae were growing exponentially. Thus, the indicator- and smartphone-based system detected the activity surge prior to a bloom. According to the researchers, the method could be developed for real-time field monitoring and prediction.