Soundscape analysis using eco-acoustic indices for the birds biodiversity assessment in urban parks (case study: Isfahan City, Iran)

Biophony and anthrophony analysis as part of the urban soundscape is an efficient approach to bird biodiversity monitoring and to studying the impact of noise pollution in urban parks. Here, we analyzed the soundscape composition to monitor the diversity of birds using acoustic indices and machine learning in 21 urban parks of Isfahan, Iran, in spring 2019. To achieve this purpose four-step method was considered: (i) choosing parks and sampling of sound and bird species; (ii) calculated the six acoustic indices; (iii) calculated the six biodiversity indices; and (iv) statistical analysis for predicting biodiversity index from acoustic indices. Three regression models including support vector machine (SVM), random forest (RF), and elastic net regularization (GLMNET) applied the acoustic indices with minimum and maximum recorded thresholds to feature extraction to measure biodiversity indicators. The optimization model was applied to reduce the independent variables. Generally, more than 18,000 samples were modeled for the dependent variables in each model. The regression results demonstrated that the highest R square was related to the songbird (0.93), evenness (0.92), and richness (0.9) indecies in the SVM model and the Shannon index (0.86) in the RF model. The results of acoustics analysis demonstrated that the Acoustic Entropy Index (H), Normalized Difference Soundscape Index (NDSI), Bioacoustics Index (BI), and Acoustic Complexity Index (ACI) indices were suitable because they could serve as proxies for bird richness and activity that reflect differences in habitat quality. Our findings offer using acoustic indicators as an efficient approach for monitoring bird biodiversity in urban parks.

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.

Protective effects of copper against aluminum toxicity on acetylcholinesterase and catecholamine contents of different regions of rat's brain

The probable protective effects of copper on the acetylcholinesterase activity and the catecholamine levels in cerebellum, cortex and mid-brain of rat, which was intoxicated by aluminum, were studied during short and long terms. In this respect, male Wistar rats weighing 200-250 g were received daily intraperitoneal doses of aluminum, copper and also combined doses of both metals for 15 days (Al 10 mg kg(-1) BW and Cu 1 mg kg(-1) BW), 30 days (Al 5 mg kg(-1) BW and Cu 0.5 mg kg(-1) BW) and 60 days (Al 1 mg kg(-1) BW and Cu 0.1 mg kg(-1) BW), respectively. The results obtained from the short period of exposure (15 days) showed that aluminum produced significant (P < 0.05) decreases in the acetylcholinesterase activity by 24.14, 23.30 and 25.81 %. Similarly, the catecholamine levels were reduced by 10.69, 12.25 and 12.64 % in cerebellum, cortex and mid-brain, respectively. Treatment with copper increases both acetylcholinesterase activity and catecholamine contents of cerebellum, cortex and mid-brain. Simultaneous injection of copper and aluminum increased both acetylcholinesterase activity and catecholamine contents in all three parts of rat brain when compared to aluminum-treated group. Same results were also observed following 30 and 60 days of exposures. In overall, it has been found that copper may have a protective-like ability to hinder aluminum toxicity in the brain.