The Influence of Transport Infrastructure Development on Bird Diversity and Abundance

Noise pollution prediction and seasonal comparison in urban parks using a coupled GIS- artificial neural network model

Noise pollution is a challenging environmental issue in densely built urban areas and requires a holistic understanding of its sources and alleviation processes. Taking Isfahan City in Iran as a typical case, this study developed a combined GIS-artificial neural network (ANN) model to predict the spatio-temporal contribution of low-width parks to poise pollution mitigation. The 30-min equivalent sound level was measured at 100 stations in six urban parks (with a total area of 55.84 ha) under stable and controlled winter and summer conditions. The noise level predicting variables were hypothesized to be the area of vegetation cover; NDVI-based vegetation density and standard deviation (std); vegetation height; and road coverage measured within 100-, 200-, and 300-m radius buffer rings drown around each noise sampling station. These predictors were introduced to a multi-layer perceptron ANN model to identify and compare the most important noise alleviation variables among the selected predictors. The mean noise levels ranged from 67.23 to 70.57 dB. The number of vehicles showed an insignificant temporal difference, indicating that the noise source was relatively constant between the seasons. The ANN model performed satisfactorily in both seasons with SSE values of < 0.03. The Mann-Whitney U test showed a significant difference in the predicted noise levels between summer and winter. This study highlighted the efficiency of the combined GIS-ANN model in predicting distant-dependent urban processes, especially noise pollution whose levels and variability are essential in formulating urban land-use management.

Effect of Chlorides from Chemical De-Icing Agents on Soil Contamination Depending on the Distance from Road and its Effects on Living Organisms

The article deals with the contamination of soil in the vicinity of the D1 motorway caused by the application of chemical de-icing agents in winter. In the selected area, during a period of one year (from October 2017 to October 2018), soil samples were regularly collected once a month at five different distances from the road. Chloride concentrations in aqueous extracts of the soil were monitored and the degree of toxicity was established for the selected living organisms. The resulting chloride load was evaluated with respect to the precipitation activity and the amount of de-icing salt applied in the area. The highest concentrations of chlorides were found at a distance of 2 m from the road. At the distance of 20 m from the road, the concentration of chlorides in the soil was approaching the chloride concentration found in the referential background set for the selected locality. The concentrations of chlorides at the first three measured distances from the road corresponded to the quantity of de-icing salt applied and the precipitation activity recorded during the relevant months. The maximum concentrations were reached in April 2018. Ecotoxicological testing of aqueous extracts of soil did not confirm any significant toxicity to the selected living organisms. From the tested organisms, the white mustard was identified to be the most sensitive to this type of toxicity; the increased toxicity was observed only for aqueous extracts of soil samples collected at distances dI (0 m) and dII (1 m), thus, it could have been related to the increased concentration of chlorides during the relevant period.

Modelling in the Context of an Environmental Mobilisation: A Graph-Based Approach for Assessing the Landscape Ecological Impacts of a Highway Project

The construction of highways leads to several environmental and landscape impacts, including the fragmentation of natural habitats for many animal species. Highway projects are therefore generally accompanied by mobilisations from the inhabitants of the areas concerned and environmental associations. This work aims to model the potential impacts of a highway project in France on ecological networks and to study the reception of the results by the opponents of this project. We have adopted a three-step approach. First, a land-cover map of the study area was produced at a fine scale of 10 m resolution. Second, we developed a multi-species approach by defining fifteen species groups representative of different habitats of our study area. Third, the design of landscape graphs and the resulting calculation of connectivity metrics allowed mapping the impact of the highway on multi-species ecological connectivity. Reflexive feedback from comments on these results by the public during a mobilisation day against the highway project allows assessment of the relevance of such a modelling approach in this context.