Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO2. In agricultural soils, N2O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N2O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N2O emission, and to compare the performances of RI: with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N2O emissions from local information. (c) 2013 Elsevier Ltd. All rights reserved.

Advanced oxidation processes (AOPs) based on formation of free radicals at ambient temperature and pressure are effective for treating endocrine disrupting chemicals (EDCs) in waters. In this study, we systematically investigated the degradation kinetics of bisphenol A (BPA), a representative EDC by hydroxyl radical (OH) with a combination of experimental and theoretical approaches. The second–order rate constant (k) of BPA with OH was experimentally determined to be 7.2 ± 0.34 × 109 M−1 s−1 at pH 7.55. We also calculated the thermodynamic and kinetic behaviors for the bimolecular reactions by density functional theory (DFT) using the M05–2X method with 6–311++G** basis set and solvation model based on density (SMD). The results revealed that H–abstraction on the phenol group is the most favorable pathway for OH. The theoretical k value corrected by the Collins–Kimball approach was determined to be 1.03 × 1010 M−1 s−1, which is in reasonable agreement with the experimental observation. These results are of fundamental and practical importance in understanding the chemical interactions between OH and BPA, and aid further AOPs design in treating EDCs during wastewater treatment processes.

Previously recommended rhizosphere-based method (RHIZO) applied to moist rhizosphere soils was integrated with moist bulk soils, and termed adjusted-RHIZO method (A-RHIZO). The A-RHIZO and RHIZO methods were systematically compared with EDTA, DTPA, CaCl2 and the first step of the Community Bureau of Reference (BCR1) methods for assessing metal phytoavailability under field conditions. Results suggested that moist bulk soils are equally suited or even better than rhizosphere soils to estimate metal phytoavailability. The A-RHIZO method was preferred to other methods for predicting the phytoavailability of Ni, Cu, Zn, Cd, Pb and Mn to wheat roots with correlation coefficients of 0.730 (P < 0.001), 0.854 (P < 0.001), 0.887 (P < 0.001), 0.739 (P < 0.001), 0.725 (P < 0.001) and 0.469 (P < 0.05), respectively. When including soil properties, other extraction methods were also able to predict phytoavailability reasonably well for some metals. Soil pH, organic matter and Fe-Mn oxide contents, and cation-exchange capacity mostly influenced the extraction and phytoavailability of metals. An adjusted-RHIZO method was the most promising approach for predicting metal phytoavailability to wheat under field conditions.

Denitrification is considered as the dominant nitrogen (N) removing pathway, however, anaerobic oxidation of ammonium (anammox) also plays a significant part in N loss in agricultural ecosystems. Large N inputs into agricultural soils may stimulate the growth of anammox bacteria, resulting in high activity and diversity of anammox bacteria and subsequent more N loss. In some specific niches, like oxic-anoxic interface, three processes, nitrification, anammox and denitrification couple with each other, and significant anammox reaction could be observed. Soil parameters like pH, dissolved oxygen, salinity, oxidation-reduction potential (ORP), and substrate concentrations impact the anammox process. Here we summarize the current knowledge on anammox activity and contribution to N loss, abundance and diversity of anammox bacteria, factors affecting anammox, and the relationship between anammox and other N loss pathways in agricultural soils. We propose that more investigations are required for (1) the role of anammox to N loss with different agricultural management strategies; (2) microscale research on the coupling of nitrification-anammox-denitrification, that might be a very complex process but ideal model for further studies responsible for N cycling in terrestrial ecosystems; and (3) new methods to estimate differential contributions of anammox, codenitrification and denitrification in total N loss in agricultural ecosystems. New research will provide much needed information to quantify the contribution of anammox in N loss from soils at landscape, ecosystem and global scales.

In this work we have investigated in details the process of degradation of the 4-amino-6-chlorobenzene-1,3-disulfonamide (ABSA), stable hydrolysis product of frequently used pharmaceutical hydrochlorothiazide (HCTZ), as one of the most ubiquitous contaminants in the sewage water. The study encompassed investigation of degradation by hydrolysis, photolysis, and photocatalysis employing commercially available TiO₂ Degussa P25 catalyst. The process of direct photolysis and photocatalytic degradation were investigated under different type of lights. Detailed insights into the reactive properties of HCTZ and ABSA have been obtained by density functional theory calculations and molecular dynamics simulations. Specifically, preference of HCTZ towards hydrolysis was confirmed experimentally and explained using computational study. Results obtained in this study indicate very limited efficiency of hydrolytic and photolytic degradation in the case of ABSA, while photocatalytic degradation demonstrated great potential. Namely, after 240 min of photocatalytic degradation, 65% of ABSA was mineralizated in water/TiO₂ suspension under SSI, while the nitrogen was predominantly present as NH4+. Reaction intermediates were studied and a number of them were detected using LC-ESI-MS/MS. This study also involves toxicity assessment of HCTZ, ABSA, and their mixtures formed during the degradation processes towards mammalian cell lines (rat hepatoma, H-4-II-E, human colon adenocarcinoma, HT-29, and human fetal lung, MRC-5). Toxicity assessments showed that intermediates formed during the process of photocatalysis exerted only mild cell growth effects in selected cell lines, while direct photolysis did not affect cell growth.

Underground coal fires start naturally or as a result of human activities. Besides burning away the important non-renewable energy resource and causing financial losses, burning coal seams emit carbon dioxide, carbon monoxide, sulfur oxide and methane, and is a leading cause of smog, acid rain, global warming, and air toxins. In the U.S. alone, the combined cost of coal-fire remediation projects that have been completed, budgeted, or projected by the U.S. Department of the Interior's Office of Surface Mining Remediation and Enforcement (OSM), exceeds $1 billion. It is estimated that these fires generate as much as 3% of the world's annual carbon dioxide emissions and consume as much as 5% of its minable coal. Considering the magnitude of environmental impact and economic loss caused by burning underground coal seams, we have developed a new, safe, reliable surface measurement of coal fire gases to assess the nature of underground coal fires. We use a drone mounted with gas sensors. Drone collected gas concentration data provides a safe alternative for evaluating the rank of a burning coal seam. In this study, a new method of determining coal rank by gas ratios is developed. Coal rank is valuable for defining parameters of a coal seam such as burn temperature, burn rate, and volume of burning seam.

A new methodology has been developed for the determination of secondary alkane sulfonates (SAS), an anionic surfactant, in environmental matrices. Sediment and sludge samples were extracted using pressurized liquid extraction and sonication, whereas wastewater and surface water samples were processed using solid-phase extraction. Extraction recoveries were acceptable for both aqueous (78–120%) and solid samples (83–100%). Determination of SAS was carried out by high or ultra performance liquid chromatography – mass spectrometry using ion trap and time-of-flight detectors. The methodology was applied to samples from Guadalete River (SW Spain), where SAS concentrations below 1 μg L−1 were measured in surface water, and from 72 to 9737 μg kg−1 in sediments. Differential partitioning was observed for SAS homologues as those having a longer hydrocarbon chain which preferentially sorbed onto particulate matter. A preliminary environmental risk assessment also showed that SAS measured levels were not harmful to the aquatic community in the sampling area.

Pipeline systems used to transport petroleum products represent a potential source of soil pollution worldwide. The design of new techniques that may improve current monitoring of pipeline leakage is imperative. This paper assesses the remote detection of small leakages of liquid hydrocarbons indirectly, through the analysis of spectral features of contaminated plants. Leaf and canopy spectra of healthy plants were compared to spectra of plants contaminated with diesel and gasoline, at increasing rates of soil contamination. Contamination effects were observed both visually in the field and thorough changes in the spectral reflectance patterns of vegetation. Results indicate that the remote detection of small volumes of gasoline and diesel contaminations is feasible based on the red edge analysis of leaf and canopy spectra of plants. Brachiaria grass ranks as a favourable choice to be used as an indicator of HCs leakages along pipelines.

Compound contamination and toxicity interaction necessitate the development of models that have an insight into the combined toxicity of chemicals. In this paper, a novel and simple model dependent only on the mixture information (MIM), was developed. Firstly, the concentration–response data of seven groups of binary and multi-component (pseudo-binary) mixtures with different mixture ratios to Vibrio qinghaiensis sp.-Q67 were determined using the microplate toxicity analysis. Then, a desirable non-linear function was selected to fit the data. It was found that there are good linear correlations between the location parameter (α) and mixture ratio (p) of a component and between the steepness (β) and p. Based on the correlations, a mixture toxicity model independent of pure component toxicity profiles was built. The model can be used to accurately estimate the toxicities of the seven groups of mixtures, which greatly simplified the predictive procedure of the combined toxicity.

This report describes a simple chemical free method that was successfully used by a team of European and Indian scientists (www.qub.ac.uk/tipot) to remove arsenic (As) from groundwater in a village in West Bengal, India. Six such plants are now in operation and are being used to supply water to the local population (www.insituarsenic.org). The study was conducted in Kasimpore, a village in North 24 Parganas District, approximately 25 km from Kolkata. In all cases, total As in treated water was less than the WHO guideline value of 10 μg L−1. The plant produces no sludge and the operation cost is 1.0 US$ per day for producing 2000 L of potable water. This work presents the chemical free arsenic removal method from groundwater and its successful implementation in West Bengal for community water supply.