Identifying diffused nitrate sources in a stream in an agricultural field using a dual isotopic approach.

Nitrate (NO3(-)) pollution is a severe problem in aquatic systems in Taihu Lake Basin in China. A dual isotope approach (δ(15)NNO3(-) and δ(18)ONO3(-)) was applied to identify diffused NO3(-) inputs in a stream in an agricultural field at the basin in 2013. The site-specific isotopic characteristics of five NO3(-) sources (atmospheric deposition, AD; NO3(-) derived from soil organic matter nitrification, NS; NO3(-) derived from chemical fertilizer nitrification, NF; groundwater, GW; and manure and sewage, M&S) were identified. NO3(-) concentrations in the stream during the rainy season [mean±standard deviation (SD)=2.5±0.4mg/L] were lower than those during the dry season (mean±SD=4.0±0.5mg/L), whereas the δ(18)ONO3(-) values during the rainy season (mean±SD=+12.3±3.6‰) were higher than those during the dry season (mean±SD=+0.9±1.9‰). Both chemical and isotopic characteristics indicated that mixing with atmospheric NO3(-) resulted in the high δ(18)O values during the rainy season, whereas NS and M&S were the dominant NO3(-) sources during the dry season. A Bayesian model was used to determine the contribution of each NO3(-) source to total stream NO3(-). Results showed that reduced N nitrification in soil zones (including soil organic matter and fertilizer) was the main NO3(-) source throughout the year. M&S contributed more NO3(-) during the dry season (22.4%) than during the rainy season (17.8%). AD generated substantial amounts of NO3(-) in May (18.4%), June (29.8%), and July (24.5%). With the assessment of temporal variation of diffused NO3(-) sources in agricultural field, improved agricultural management practices can be implemented to protect the water resource and avoid further water quality deterioration in Taihu Lake Basin.

Holographic quantitative structure-activity relationship for prediction acute toxicity of benzene derivatives to the guppy (Poecilia reticulata).

Holographic quantitative structure-activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encoded the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. In this paper, the acute toxicity data to the guppy (Poecilia reticulata) for a series of 56 substituted benzenes, phenols, aromatic amines and nitro-aromatics were subjected and this resulted in a model with a high predictive ability. The influence of fragment size and fragment distinction parameters on the quality of HQSAR model was investigated. The robustness and predictive ability of the model were also validated by leave-one-out (LOO) cross-validation procedure and external testing data set.