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Lasun T. Ogundele Oyediran K. Owoade Felix S. Olise Philip K. Hopke 《Environmental monitoring and assessment》2016,188(10):574
To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM2.5 and PM2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m3 for PM2.5 and 331.36, 190.01, and 184.60 μg/m3 for PM2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area. 相似文献
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Nitrogen enrichment in runoff sediments as affected by soil texture in Beijing mountain area 总被引:3,自引:0,他引:3
Yang Yang Zhihan Ye Baoyuan Liu Xianqin Zeng Suhua Fu Bingjun Lu 《Environmental monitoring and assessment》2014,186(2):971-978
Enrichment ratio (ER) is widely used in nonpoint source pollution models to estimate the nutrient loss associated with soil erosion. The objective of this study was to determine the ER of total nitrogen (ERN) in the sediments eroded from the typical soils with varying soil textures in Beijing mountain area. Each of the four soils was packed into a 40 by 30 by 15 cm soil pan and received 40-min simulated rainfalls at the intensity of 90 mm h?1 on five slopes. ERN for most sediments were above unity, indicating the common occurrence of nitrogen enrichment accompanied with soil erosion in Beijing mountain area. Soil texture was not the only factor that influenced N enrichment in this experiment since the ERN for the two fine-textured soils were not always lower. Soil properties such as soil structure might exert a more important influence in some circumstances. The selective erosion of clay particles was the main reason for N enrichment, as implied by the significant positive correlation between the ER of total nitrogen and clay fraction in eroded sediments. Significant regression equations between ERN and sediment yield were obtained for two pairs of soils, which were artificially categorized by soil texture. The one for fine-textured soils had greater intercept and more negative slope. Thus, the initially higher ERN would be lower than that for the other two soils with coarser texture once the sediment yield exceeded 629 kg ha?1. 相似文献
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Gregory S. Piorkowski Rob C. Jamieson Lisbeth Truelstrup Hansen Greg S. Bezanson Chris K. Yost 《Environmental monitoring and assessment》2014,186(1):277-291
Escherichia coli can persist in streambed sediments and influence water quality monitoring programs through their resuspension into overlying waters. This study examined the spatial patterns in E. coli concentration and population structure within streambed morphological features during baseflow and following stormflow to inform sampling strategies for representative characterization of E. coli populations within a stream reach. E. coli concentrations in bed sediments were significantly different (p?=?0.002) among monitoring sites during baseflow, and significant interactive effects (p?=?0.002) occurred among monitoring sites and morphological features following stormflow. Least absolute shrinkage and selection operator (LASSO) regression revealed that water velocity and effective particle size (D 10) explained E. coli concentration during baseflow, whereas sediment organic carbon, water velocity and median particle diameter (D 50) were important explanatory variables following stormflow. Principle Coordinate Analysis illustrated the site-scale differences in sediment E. coli populations between disconnected stream segments. Also, E. coli populations were similar among depositional features within a reach, but differed in relation to high velocity features (e.g., riffles). Canonical correspondence analysis resolved that E. coli population structure was primarily explained by spatial (26.9–31.7 %) over environmental variables (9.2–13.1 %). Spatial autocorrelation existed among monitoring sites and morphological features for both sampling events, and gradients in mean particle diameter and water velocity influenced E. coli population structure for the baseflow and stormflow sampling events, respectively. Representative characterization of streambed E. coli requires sampling of depositional and high velocity environments to accommodate strain selectivity among these features owing to sediment and water velocity heterogeneity. 相似文献