Delayed fluorescence measurement

Delayed fluorescence measurement technology: live phytoplankton and ecological environment online monitoring system

Phytoplankton in river water plays a very important role in the metabolism of carbon in running water. Its net growth rate is a key factor in river ecosystems. In a dynamic environment, light, temperature and nutrients fluctuate quickly. When the water flow in a watershed is concentrated in a certain area, as the water flow slows down, the nutrients gradually enrich. Growth can easily cause blooms. Therefore, the formation of blooms is caused by the proliferation of living algae, and is affected by environmental factors such as water temperature, nutrient content, and radiation.

At present, the common detection method for water blooms is fast fluorescence technology, which measures all substances that can emit fluorescence, including dead phytoplankton and humus. Delayed fluorescence is an exclusive characteristic of photosynthesis in living cells and an indicator of photosynthetic efficiency. Delayed fluorescence technology can effectively shield the interference of resuspension, dead organisms and humus on the measurement accuracy, which cannot be achieved by other fluorescence measurement technologies. This difference between delayed fluorescence technology and ordinary fast fluorescence technology can play a decisive role in shallow water lakes or rivers. Especially those areas where re-suspension and flooding often occur, thus bringing a certain amount of degraded algae or algae without photosynthetic function into the water body. Therefore, delayed fluorescence technology has become a hot research topic in water bloom monitoring.

The live phytoplankton and ecological environment online monitoring system (DF) can monitor the delayed fluorescence of algae online, and automatically record the photosynthetic biomass and composition of live phytoplankton. It is suitable for continuous and online monitoring of the number of natural phytoplankton. These photosynthetic algae data are obtained through delayed fluorescence technology, so the system can accurately detect the formation and extinction of algae and blooms.

In order to simulate the formation and decay of various blooms, the ecological factors (NO3, NO2, COD, TOC, turbidity, water body ultraviolet, visible light and other spectral radiation) that affect live phytoplankton also need to be obtained . Using the time series data of photosynthetic sensitive algae, combined with the measured ecological factor parameters, the seasonal change pattern of phytoplankton is analyzed as a function of the dynamic changing environment. Eventually, a functional relationship between the ecological factors that change with the seasons and the growth of phytoplankton is established, so that the process of various blooms can be sufficiently simulated.

System performance index:

1. Measure algae concentration;

2. Identify four types of algae including blue-green algae (including green algae, Euglena, etc.), diatoms (including diatoms, golden algae, yellow algae, etc.) and cryptoalgae, which can be expanded to 6 types of algae, accuracy: ± 5%;

3. Can be used for HAB identification;

4. It can automatically measure the dynamic changes of photosynthetic rate in the field;

5. Measurement frequency: 6-10 times per hour.

6. Measurable water quality parameters include blue-green algae (measurement range: 0-10, 0-100ug / L, accuracy: 0.02 ug / L), chlorophyll a (measurement range: 0-10, 0-100ug / L, 0 -500ug / L, accuracy: 0.02 ug / LCDOM (measurement range: 0-20 / 200ug / L, accuracy: 0.04 ug / L), oil in water (measurement range: 0-10, 100, 500, 5000ug / L, accuracy: 0.1 ug / L), water sulfide (H2S, PH, water temperature and water depth), ultraviolet water quality (COD, BOD, TOC, nitrate, nitrous nitrogen, turbidity).

7. Available spectral wavelength range: 280-500nm (UV) or 320-950nm (UV / VIS);

Optional enhanced community identification and photosynthesis rate-light curve;

Foreign applications:

1. Stability and change of phytoplankton communities in a highly dynamic environment—the case of large, shallow Lake Balaton (Hungary)

Stability and change of phytoplankton community in highly dynamic environment—A case study of Lake Balaton, Hungary

Abstract: The key environmental factors (water temperature, total radiation, vertical attenuation of light, internal P load) and time series data of photosynthetic sensitive algae in four colors during 2003-2004 were collected in days. Use these data to analyze the seasonal change patterns of phytoplankton as a function of the dynamically changing environment. The environmental state is extracted as a point in three-dimensional space, used to identify habitat patterns that form bloom-forming communities, and find indicators of environmental stability / disturbance of the physical environment. These templates are unified into a simple limit model, which can sufficiently simulate the formation and decline of various blooms. However, under specific strong and unidirectional external forces, blooms are small probability events. The experimental quantification of disturbance and system component stability / community changes helps to distinguish between disturbance-driven succession and spontaneous succession. These two processes are equally important in forming phytoplankton composition and biomass.

2. Delayed fluorescence as a direct indicator of diurnal variation in quantum and radiant energy utilization efficiencies of phytoplankton

Delayed fluorescence as a direct indicator of the diurnal variation of phytoplankton quantum and radiation energy utilization efficiency

Abstract: Relying on natural temperature and irradiance to cultivate Chlorella vulgaris, this paper compares delayed fluorescence (DF) excitation spectroscopy with radiocarbon tracing technology. It is achieved by monitoring the DF, quantum efficiency (QE) and radiant energy utilization efficiency () index, where the radiant energy utilization efficiency is calculated by carbon absorption measurement, while using the radiocarbon tracking technology to measure carbon absorption. During the day and night cycle of algae cultivation, temperature, irradiance and chlorophyll (Chl) content need to be monitored; algae are cultivated in an open and transparent plastic tank that is submerged under the surface of Lake Kinneret in Israel. The results showed that the DF signal in the diurnal cycle was related to QE () and (r2 = 0.977, p <0.01). We suggest that in addition to measuring the composition of live chlorophyll and phytoplankton, the DF signal can provide a reference for the QE and phytoplankton.

3. Assessing phytoplankton growth in River Tisza (Hungary)

Assess the growth of phytoplankton in the Tisza River

Abstract: Phytoplankton in river water plays a very important role in the metabolism of carbon in running water. The role in material flux depends on the growth and loss of phytoplankton, so the net growth rate of phytoplankton is a key factor in river ecosystems. However, monitoring in-situ algae in situ is very complicated, because algae moves downstream with the water environment and is affected by local conditions along the route. The fluctuation of environmental variables (light, nutrition and disturbance) makes river water a dynamic habitat for phytoplankton. With the general flow of river water, the characteristic organic matter of individual river sections will temporarily select the optimal conditions to maintain the quantity, otherwise it will be more or less isolated, while slowly moving the water body as a supplementary source. The latter is called "dead" and "nearshore retention". These supplemental sources significantly affect all flowing water, so the impact can be monitored through the growth of phytoplankton in specific areas. The Tisza River is the largest tributary of the Danube. The Hungarian area is 600km long, and the lower part was used for flood prevention purposes in the 19th and early 20th centuries. Based on the phytoplankton frequency measurement and hydraulic model, we evaluated the growth of phytoplankton along the river and compared the results with the river morphology.

4. Istvánovics V., Honti M., Osztoics A., HM Shafik, Padisák J., Y. Yacobi and W. Eckert (2005) On-line delayed fluorescence excitation spectroscopy, as a tool for continuous monitoring of phytoplankton dynamics and itsapplication in shallow Lake Balaton (Hungary). Freshwater Biology 50: 1950-1970.

5. Honti M., Istvánovics V. and Osztoics A. (2005) Measuring and modelling in situ dynamic photosynthesis of various phytoplankton groups. Verh. Internat. Verein. Limnol.29: 194-196.

6. Honti M., Istvánovics V. and Osztoics A. (2007) Stability and change of phytoplankton communities in a highly dynamic environment? The case of large, shallow Lake Balaton (Hungary). Hydrobiologia 581: 225-240.

7. Honti M., Istvánovics V. and Kozma Zs. (2008) Assessing phytoplankton growth in River Tisza (Hungary). Verh. Internat. Verein. Limnol.30 (1): 87-89.

8. Istvánovics V. and Honti M. (2008) Longitudinal variability in phytoplankton and basic environmental drivers along Tisza River, Hungary. Verh. Internat. Verein. Limnol.30 (1): 105-108.