The present work reports pollution level and spatial distribution of heavy metals (HMs) i.e. Iron (Fe), Manganese (Mn), Zinc (Zn), Copper (Cu), Lead (Pb), Chromium (Cr), Nickel (Ni), and Arsenic (As) in road dust of Dehradun city, Uttarakhand, India. Seventy samples in triplicates were collected from different land-use areas categorized as residential, commercial, national highways, and silent zones. The Concentrations of studied HMs were determined by the acid digestion method followed by inductively coupled plasma-mass spectrometry (ICP-MS). Pearson's significant correlation analysis is used to evaluate the relationship between heavy metal (HM) concentration and principal components analysis (PCA) was used for source identification of HMs in road dust. The average concentration of Mn, Zn, Cu, Pb, and Ni found higher when compared to the Indian soil background values. Among all studied HMs, Pb and Zn were found the most contaminated HMs in road dust. The degree of contamination shows the highest contamination of HMs found in commercial zones followed by National highways. The pollution load index (PLI) was found higher than 1 in all monitored 70 locations, showing the deterioration in the quality of road dust over the Dehradun city due to HMs. The principal component analysis result suggests that PC1 (Fe, Zn, Cu, and Ni) mainly comes from vehicular pollution, including tire wear and brake pad wear particles and corrosion of metallic components. PC2 (Mn and As) primarily comes from fossil fuel burning and pesticides and fertilizers containing Mn and As compounds. PC3 (Pb and Cr) mainly comes into road dust via fuel and lubrication oil residues and chrome-based paints. Spatial distribution maps of the HM concentration reveal that the city's central and eastern zone is the primary hotspot of high HM concentration, which links these zones to high vehicular volume and high population pressure.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.sste.2021.100470 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.
Biomed Eng Online
January 2025
Department of Pulmonary and Critical Care Medicine, National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
Background: Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.
Methods: All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers.
Characteristics and analysis of PM2.5 particles in a light-polluted atmosphere in winter.
Sci Rep
January 2025
Yili Prefecture Product Quality Institute, Yining, 835000, China.
To study the micro-morphological characteristics of PM2.5 and its effect on ambient air quality, a 7500F scanning electron microscope (SEM) was utilized in this study to examine the micromorphology and elemental composition of PM2.5 and its impact on ambient air quality during heavily polluted weather in Yining City in the winter of 2018-2019.
View Article and Find Full Text PDFParticle Size-Dependent Monthly Variation of Pollution Load, Ecological Risk, and Sources of Heavy Metals in Road Dust in Beijing, China.
Toxics
January 2025
Beijing Key Laboratory of Resource-Oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Road dust carries various contaminants and causes urban non-point source pollution in waterbodies through runoff. Road dust samples were collected in each month in two years and then sieved into five particle size fractions. The concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in each fraction were measured.
View Article and Find Full Text PDFEfficient Degradation of Industrial Biowaste via In-Vessel Composting-Technical and Microbial Assessments.
Bioengineering (Basel)
January 2025
Department of Chemical and Environmental Engineering, University of Nottingham, Broga Road, Semenyih 43500, Selangor, Malaysia.
In this study, a pilot-scale in-vessel composter was used to treat a mixture of industrial biowaste, with soybean curd residue and saw dust as the major substrates. The composter is capable of treating up to 350 tons/month of waste, producing up to 150 tons/month of high-quality compost within a retention time of 7-10 days. The final compost has an average nitrogen-phosphorus-potassium content of 6%, moisture content of 28%, pH of 6.
View Article and Find Full Text PDFEvaluation of the water quality monitoring program of Mountain Rivers located in a tropical protected atlantic Forest area (PARNASO, Brazil): are the impacts of the highway really assessed?
Integr Environ Assess Manag
January 2025
Programa de Biologia Marinha e Ambientes Costeiros, Universidade Federal Fluminense (PBMAC-UFF), Niterói, Rio de Janeiro, Brazil.
Road activities are recognized sources of pollution that affect the hydrochemistry of nearby water bodies. This study evaluated the Water Quality Monitoring Program in the Soberbo and Iconha rivers in the Guapi-Macacu watershed, which is affected by the BR-116 highway. The Rio-Teresópolis Concessionaire from 2009 to 2016 carried out quarterly sampling.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!