The impact of environmental factors on water quality analysis has always been an important topic in scientific research. Among these factors, temperature is considered one of the key factors that may influence the determination of total nitrogen levels. This study focuses on seawater as the research object and aims to reduce the influence of temperature variations in marine environments on the measurements of an in-situ total nitrogen analyzer. Different environmental water temperatures were collected for the total nitrogen analyzer's measurement data using standard samples. The collected data was then processed using curve plots and regression analysis to establish a linear regression model for thein-situ total nitrogen analyzer. The experiment revealed that as the temperature increases, the absorbance values measured by the total nitrogen analyzer show a decreasing trend, which may lead to underestimated total nitrogen measurements. The results indicate that high temperatures can affect the stability of reagents and the chemical reactions inside the analyzer, thus affecting the measurement results. Based on these findings, a temperature correction model was introduced in this study. This study introduced a temperature correction model. The optimized method effectively reduced the impact of temperature on in-situ total nitrogen analyzer determination, thereby enhancing the accuracy and stability of measurements.
The rapid prediction of carbon content by spectral method is helpful to grasp the carbon dynamic of sediments in intertidal zone in a timely manner. The spectral reflectivity of sediments with high carbon content and low carbon content often varies widely, limiting the accuracy of the model. When sediment samples are classified according to the carbon content and modeled separately, accurate prediction of sediment carbon is expected to be achieved. However, previous studies have not reported it. In our study, sediment samples were divided into low-carbon and high-carbon sample sets according to 3 g/kg carbon content, and divided into low-carbon, medium-carbon and high-carbon sample sets according to 2 g/kg and 4 g/kg carbon content, and then were pre-processed and PLS modeled separately. It is found that these classification can improve the modeling accuracy, but not improve the prediction accuracy. These results can provide a technical reference for the prediction of sediment carbon in intertidal zone.
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