Analysis of the traffic injury severity on two-lane,two-way rural roads based on classification tree models

Two-lane, two-way roads constitute a major portion of the rural roads in most countries of the world. This study identifies the factors influencing crash injury severity on these roads in Iran. Classification and regression trees (CART), which is one of the most common methods of data mining, was employed to analyze the traffic crash data of the main two-lane, two-way rural roads of Iran over a 3-year period (2006–2008). In the analysis procedure, the problem of three-class prediction was decomposed into a set of binary prediction models, which resulted in a higher overall accuracy of the predictions of the model. In addition, the prediction accuracy of the fatality class, which was nearly 0% in some of the previous studies, increased significantly. The results indicated that improper overtaking and not using a seatbelt are the most important factors affecting the severity of injuries.     

A comprehensive review of hydrophobic silica and composite aerogels: synthesis,properties and recent progress towards environmental remediation and biomedical applications

Environmental Science and Pollution Research - The hydrophobicity of silica and composite aerogels has enabled them to acquire applications in a variety of fields. With remarkable structural,...     

Multivariate analysis of accumulation and critical risk analysis of potentially hazardous elements in forage crops

Environmental Monitoring and Assessment - Few estuaries remain unaffected by water management and altered freshwater deliveries. The Caloosahatchee River Estuary is a perfect case study for...     

Urban noise assessment and its nonauditory health effects on the residents of Chiniot and Jhang,Punjab, Pakistan

Environmental Science and Pollution Research - Elevated noise level is an emerging global problem. Therefore, the present work is conducted that can improve, increase, and integrate the already...     

Environmental impacts of new Coronavirus outbreak in Iran with an emphasis on waste management sector

Journal of Material Cycles and Waste Management - The COVID-19 pandemic have brought several environmental problems worldwide, among which management of municipal solid wastes (MSW) is of great...     

Reviewing the strategies for climate change and sustainability after the US defiance of the Paris Agreement: an AHP–GMCR-based conflict resolution approach

Environment, Development and Sustainability - In the landmark Paris Agreement, the global economies agreed to put forward their best efforts in mitigation and adaptation of climate change. The...     

Single and joint oxidative stress–related toxicity of sediment-associated cadmium and lead on Bellamya aeruginosa

Environmental Science and Pollution Research - The biotoxicity of heavy metals in sediments toward benthic organisms has evoked great concern for the health of freshwater ecosystems. This study...     

Evaluation of fresh and stored rainwater quality in fluoride and arsenic endemic area of Thar Desert,Pakistan

In the current study, the chemistry of fresh and stored rainwater of Thar Desert, Pakistan, was estimated during two consecutive monsoon periods. The present research deals with the variation in physicochemical parameters, total arsenic (As^t), inorganic arsenic species (Asⁱ, As^V, As^III), and fluoride (F^?) in stored rainwater (SRW) at different time intervals (1 week to 3 months). The pH of fresh rainwater (FRW) samples showed slightly acidic to neutral in nature (6.08–7.06) which were inconsistent with the reference pH value (5.6) of rainwater. The resulted data indicated that As^t and F^? levels in SRW were enhanced with time duration. The levels of As^t and F^? in SRW after different time intervals were found in the range of 194–683 μg/L and 10–35.4 mg/L, respectively. The values of As^t and F^? were 20–70 and 7–24 times higher than those of WHO permissible limits, 10 μg/L and 1.5 mg/L, respectively. The As^III was dominant species in SRW, which corresponds to >60 % of Asⁱ. The characteristics of the SRW revealed an unacceptable quality to consume for drinking and agricultural purposes in the studied area.     

Health risk assessment of occupational exposure to styrene in Neyshabur electronic industries

Environmental Science and Pollution Research - Styrene is one of the essential components in making thousands of everyday products. Occupational exposure to styrene causes pulmonary, neurological,...     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.