To identify different NO₃ ⁻ sources in surface water and to estimate their proportional contribution to the nitrate mixture in surface water, a dual isotope and a Bayesian isotope mixing model have been applied for six different surface waters affected by agriculture, greenhouses in an agricultural area, and households. Annual mean δ¹⁵N–NO₃ ⁻ were between 8.0 and 19.4‰, while annual mean δ¹⁸O–NO₃ ⁻ were given by 4.5–30.7‰. SIAR was used to estimate the proportional contribution of five potential NO₃ ⁻ sources (NO₃ ⁻ in precipitation, NO₃ ⁻ fertilizer, NH₄ ⁺ in fertilizer and rain, soil N, and manure and sewage). SIAR showed that “manure and sewage” contributed highest, “soil N”, “NO₃ ⁻ fertilizer” and “NH₄ ⁺ in fertilizer and rain” contributed middle, and “NO₃ ⁻ in precipitation” contributed least. The SIAR output can be considered as a “fingerprint” for the NO₃ ⁻ source contributions. However, the wide range of isotope values observed in surface water and of the NO₃ ⁻ sources limit its applicability.

This paper describes a novel statistical approach to derive ecologically relevant sediment quality guidelines (SQGs) from field data using a nonparametric empirical Bayesian method (NEBM). We made use of the Norwegian Oil Industrial Association database and extracted concurrently obtained data on species density and contaminant levels in sediment samples collected between 1996 and 2001. In brief, effect concentrations (ECs) of each installation (i.e., oil platform) at a given reduction in species density were firstly derived by fitting a logistic-type regression function to the relationship between the species density and the corresponding concentration of a chemical of concern. The estimated ECs were further improved by the NEBM which incorporated information from other installations. The distribution of these improved ECs from all installations was determined nonparametrically by the kernel method, and then used to determine the hazardous concentration (HC) which can be directly linked to the species loss (or the species being protected) in the sediment. This method also enables an accurate estimation of the lower confidence limit of the HC, even when the number of observations was small. To illustrate the effectiveness of this novel technique, barium, cadmium, chromium, copper, mercury, lead, tetrahydrocannabinol, and zinc were chosen as example contaminants. This novel approach can generate ecologically sound SQGs for environmental risk assessment and cost-effectiveness analysis in sediment remediation or mud disposal projects, since sediment quality is closely linked to species density.

Although generally misunderstood, the p value is the probability of the test results or more extreme results given H₀ is true: it is not the probability of H₀ being true given the results. To obtain directly useful insight about H₀, the positive predictive value (PPV) and the negative predictive value (NPV) may be useful extensions of null hypothesis significance testing (NHST). They provide information about the probability of statistically significant and non-significant test outcomes being true based on an a priori defined biologically meaningful effect size. The present study explores the utility of PPV and NPV in an ecotoxicological context by using the frequently applied Daphnia magna reproduction test (OECD guideline 211) and the chemical stressor lindane as a model system. The results indicate that especially the NPV deviates meaningfully between a test design strictly following the guideline and an experimental procedure controlling for α and β at the level of 0.05. Consequently, PPV and NPV may be useful supplements to NHST that inform the researcher about the level of confidence warranted by both statistically significant and non-significant test results. This approach also reinforces the value of considering α, β, and a biologically meaningful effect size a priori.

Carbon monoxide (CO) is of great interest as a restriction factor for pollutants related to incomplete combustions. This study attempted to evaluate CO emission in East China using the analytical Bayesian inverse method and observations at Mount Hua in springtime. The mixing ratio of CO at the receptor was calculated using 5-day source-receptor relationship (SRR) simulated by a Lagrangian Particle Dispersion Model (FLEXPART) and CO emission flux. The stability of the inversion solution was evaluated on the basis of repeated random sampling simulations. The inversion results demonstrated that there were two city cluster regions (the Beijing–Tianjin–Hebei region and the low reaches of the Yangtze River Delta) where the difference between a priori (Intercontinental Chemical Transport Experiment-Phase B, INTEX-B) and a posteriori was statistically significant and the a priori might underestimate the CO emission flux by 37 %. A correction factor (a posteriori/a priori) of 1.26 was suggested for CO emission in China in spring. The spatial distribution and magnitude of the CO emission flux were comparable to the latest regional emission inventory in Asia (REAS2.0). Nevertheless, further evaluation is still necessary in view of the larger uncertainties for both the analytical inversion and the bottom-up statistical approaches.

In order to reduce the losses by water pollution, forewarning model for water pollution risk based on Bayes theory was studied. This model is built upon risk indexes in complex systems, proceeding from the whole structure and its components. In this study, the principal components analysis is used to screen out index systems. Hydrological model is employed to simulate index value according to the prediction principle. Bayes theory is adopted to obtain posterior distribution by prior distribution with sample information which can make samples’ features preferably reflect and represent the totals to some extent. Forewarning level is judged on the maximum probability rule, and then local conditions for proposing management strategies that will have the effect of transforming heavy warnings to a lesser degree. This study takes Taihu Basin as an example. After forewarning model application and vertification for water pollution risk from 2000 to 2009 between the actual and simulated data, forewarning level in 2010 is given as a severe warning, which is well coincide with logistic curve. It is shown that the model is rigorous in theory with flexible method, reasonable in result with simple structure, and it has strong logic superiority and regional adaptability, providing a new way for warning water pollution risk.

Industrial activity is one of the main sources of ambient pollution in developed countries. However, research analyzing its effect on birth outcomes is inconclusive. We analyzed the association between proximity of mother's municipality of residence to industries from 24 different activity groups and risk of very (VPTB) and moderate (MPTB) preterm birth, very (VLBW) and moderate (MLBW) low birth weight, and small for gestational age (SGA) in Spain, 2004-2008. An ecological study was defined, and a "near vs. far" analysis (3.5 km threshold) was carried out using Hierarchical Bayesian models implemented via Integrated Nested Laplace Approximation. VPTB risk was higher for mothers living near pharmaceutical companies. Proximity to galvanization and hazardous waste management industries increased the risk of MPTB. Risk of VLBW was higher for mothers residing near pharmaceutical and non-hazardous or animal waste management industries. For MLBW many associations were found, being notable the proximity to mining, biocides and animal waste management plants. The strongest association for SGA was found with proximity to management animal waste plants. These results highlight the importance of further research on the relationship between proximity to industrial sites and the occurrence of adverse birth outcomes especially for the case of pharmaceutical and animal waste management activities.