Root zone microbial populations, urease activities, and purification efficiency for a constructed wetland

In order to investigate the effects of microorganisms and their urease activities in macrophytic root zones on pollutant removal, four small-scale plots (SSPs) of vertical/reverse-vertical flow wetlands were set up to determine: a) the relationship between the abundance of microorganisms in the root zones and water purification efficiency; and b) the relationship between urease activities in the root zones and pollutant removal in a constructed wetland system. Total numbers of the microbial population (bacteria, fungi, and actinomyces) along with urease activities in the macrophytic root zones were determined. In addition, the relationships between microbial populations and urease activities as well as the wastewater purification efficiencies of total phosphorus (TP), total Kjeldahl nitrogen (TKN), biochemical oxygen demand in 5 days (BOD5), and chemical oxygen demand (COD) were also analyzed. The results showed that there was a highly significant positive correlation (r = 0.9772, P < 0.01) between the number of bacteria in the root zones and BOD5 removal efficiency and a significant negative correlation (r = -0.9092, P < 0.05) between the number of fungi and the removal efficiency of TKN. Meanwhile, there was a significant positive correlation (r = 0.8830, P < 0.05) between urease activities in the root zones and the removal efficiency of TKN. Thus, during wastewater treatment in a constructed wetland system, microorganism and urease activities in the root zones were very important factors.

Fast Discrimination of Commerical Corn Varieties Based on Near Infrared Spectra

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenurnber range from 4 000 to 12 000 cm (1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.