The influence of long term trends in pollutant emissions on deposition of sulphur and nitrogen and exceedance of critical loads in the United Kingdom

In the United Kingdom, as with other European countries, land-based emissions of NO_x and SO₂ have fallen significantly over the last few decades. SO₂ emissions fell from a peak of 3185 Gg S in 1970 to 344 Gg S in 2005 and are forecast by business-as-usual emissions scenarios to fall to 172 Gg by 2020. NO_x emissions were at a maximum of 951 Gg N in 1970 and fell to 378 by 2005 with a further decrease to 243 Gg N forecast by 2020. These large changes in emissions have not been matched by emissions changes for NH₃ which decreased from 315 Gg N in 1990 to 259 in 2005 and are forecast to fall to 222 by 2020. The Fine Resolution Atmospheric Multi-pollutant Exchange model (FRAME) has been applied to model the spatial distribution of sulphur and nitrogen deposition over the United Kingdom during a 15-year time period (1990–2005) and compared with measured deposition of sulphate, nitrate and ammonium from the national monitoring network. Wet deposition of nitrogen and sulphur was found to decrease more slowly than the emissions reductions rate. This is attributed to a number of factors including increases in emissions from international shipping and changing rates of atmospheric oxidation. The modelled time series was extended to a 50-year period from 1970 to 2020. The modelled deposition of SO_x, NO_y and NH_x to the UK was found to fall by 87%, 52% and 25% during this period. The percentage area of sensitive habitats in the United Kingdom for which critical loads are exceeded is estimated to fall from 85% in 1970 to 37% in 2020 for acidic deposition and from 73% to 49% for nutrient nitrogen deposition. The significant reduction in land emissions of SO₂ and NO_x focuses further attention in controlling emissions from international shipping. Future policies to control emissions of ammonia from agriculture will be required to effect further significant reductions in nitrogen deposition.     

Estimating Evapotranspiration for Dryland Cropping Systems in the Semiarid Texas High Plains Using SWAT

The Soil and Water Assessment Tool (SWAT) is one of the most widely used watershed models for simulating hydrology in response to agricultural management practices. However, limited studies have been performed to evaluate the SWAT model's ability to estimate daily and monthly evapotranspiration (ET) in semiarid regions. ET values were simulated using ArcSWAT 2012 for a lysimeter field managed under dryland conditions at the USDA‐ARS Conservation and Production Research Laboratory at Bushland, Texas, and compared with measured lysimeter values from 2000 to 2010. Two scenarios were performed to compare SWAT's performance: (1) use of default plant leaf area index (LAI) values in the embedded plant database and (2) adjusted LAI values. Scenario 1 resulted in an “unsatisfactory” Nash‐Sutcliffe efficiency (NSE) of 0.42 and 0.38 for the calibration and validation periods, respectively. Scenario 2 resulted in a “satisfactory” NSE value for the calibration period while achieving a “good” NSE of 0.70 for the validation period. SWAT generally underestimated ET at both the daily and monthly levels. Overestimation during fallow years may be due to the limitations of the pothole function used to simulate furrow diking. Users should be aware of potential errors associated with using default LAI parameters. Inaccuracies in ET estimation may also stem from errors in the plant stress functions, particularly when evaluating water management practices for dryland watersheds.     

Content of selected elements and low-molecular-weight organic acids in fruiting bodies of edible mushroom Boletus badius (Fr.) Fr. from unpolluted and polluted areas

Environmental Science and Pollution Research - The aim of the study was to (i) investigate the potential of edible mushroom Boletus badius (Fr.) Fr. to accumulate 53 elements from unpolluted acidic...     

Concentration,sources and risk assessment of PAHs in bottom sediments

Environmental Science and Pollution Research - The aims of the study were to investigate the concentration, sources and ecological risk of PAHs (polycyclic aromatic hydrocarbons) in bottom...     

Unexpected difference in phenol sorption on PTMA- and BTMA-bentonite

The comparison of phenol sorption on phenyltrimethylammonium (PTMA)- and benzyltrimethylammonium (BTMA)-bentonite shows a clear difference as far as phenol sorption isotherms are concerned. For PTMA-bentonite the sorption isotherm is of a straight-line character which results from simple partitioning of phenol between the aqueous and organic phases sorbed on the bentonite surface. For BTMA-bentonite the isotherm has a convex shape, characteristic of physicochemical sorption.For the first time a three-parametric model, including the dissociation constant of phenol pK_a, distribution constant of phenol Kd_phen and phenolate anion Kd_phen⁻ between the aqueous phase and the bentonite phases is used for the evaluation of phenol sorption on organoclays with pH change. The model shows that the values of Kd_phen are higher than those of Kd_phen⁻ for all investigated initial phenol concentrations.The inspection of the FTIR spectrum of BTMA-bentonite loaded with phenol in the regions 1300–1600 and 1620–1680 cm⁻¹ shows the features of π–π electron interaction between the benzene rings of phenol and the BTMA cation together with the phenol–water hydrogen bond strengthened by this interaction.     

Studies of the effect of simulated acid rain on the ecological equilibrium of the soil system

Introduction to the problem of environmental pollution caused by acid depositions. Presentation of principles of the six-year field experiment with simulated acid rain. Short characteristics of particular parts of the series of papers.     

Applying deep learning to right whale photo identification

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.