Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?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 PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?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 PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?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.     

The use of sensory perception indicators for improving the characterization and modelling of total petroleum hydrocarbon (TPH) grade in soils

This paper proposes a multistep approach for creating a 3D stochastic model of total petroleum hydrocarbon (TPH) grade in potentially polluted soils of a deactivated oil storage site by using chemical analysis results as primary or hard data and classes of sensory perception variables as secondary or soft data. First, the statistical relationship between the sensory perception variables (e.g. colour, odour and oil–water reaction) and TPH grade is analysed, after which the sensory perception variable exhibiting the highest correlation is selected (oil–water reaction in this case study). The probabilities of cells belonging to classes of oil–water reaction are then estimated for the entire soil volume using indicator kriging. Next, local histograms of TPH grade for each grid cell are computed, combining the probabilities of belonging to a specific sensory perception indicator class and conditional to the simulated values of TPH grade. Finally, simulated images of TPH grade are generated by using the P-field simulation algorithm, utilising the local histograms of TPH grade for each grid cell. The set of simulated TPH values allows several calculations to be performed, such as average values, local uncertainties and the probability of the TPH grade of the soil exceeding a specific threshold value.     

Spatial and temporal variability of metals in inter-tidal beach sediment of Mumbai, India

Nine metals were monitored in the beach sediment in Mumbai from May 2011 to March 2012 to evaluate the spatial and temporal distributions. The average heavy metal concentrations exhibited the following order: Fe > Mn > Cr > Co > Ni > Pb > Zn > Cu > Cd for the four sampling sites. The mean concentrations (± SD) of Fe, Mn, Cr, Co, Ni, Pb, Zn, Cu and Cd were estimated to be 31.15?±?10.02 g kg^?1, 535.04?±?76.42, 151.98?±?97.90, 92.76?±?14.18, 67.52?±?11.32, 59.57?±?15.19, 54.65?±?15.01, 32.24?±?8.07 and 18.75?±?1.76 mg kg^?1, respectively. The results indicated that the sediments were polluted with Cd, Cr, Co and Pb due to high anthropogenic influences. Spatial variation of metals revealed that most of the metals were high in Dadar beach and low in Aksa beach. Cd was the highest contaminant metal studied with a mean contamination factor of 93.75. The pollution load indices of the studied beaches ranged from 1.63 (Aksa) to 1.91 (Dadar) and indicated that the beach sediments were polluted with heavy metals. The heavy metal contents increased in relation to monsoon, and most of the heavy metals showed significantly high concentrations in November during the post-monsoon. The statistical analysis revealed significant effect of study site on all the metals studied. Further, there was a significant difference on metal accumulation on bimonthly basis in relation to weather pattern in Mumbai beaches.     

Characterizing spatial structure of sediment E. coli populations to inform sampling design

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.     

Carbon, nitrogen, and phosphorus stoichiometry of plankton and the nutrient regime in Cabo Frio Bay, SE Brazil

This long-term study, performed during the years 2003–2005 and 2008–2009, investigated the carbon (C), nitrogen (N), and phosphorus (P) contents of the phyto- and zooplankton communities and the nutrient regime of Cabo Frio Bay, SE Brazil. The information intends to serve as baseline of the plankton C, N, and P stoichiometry for the calibration of biogeochemical and ecological models in support to future findings related to the local and regional phenomena of climatic change. Cabo Frio Bay is a small semienclosed system set adjacent to a region subject to sporadic coastal upwelling. Zooplankton exhibited average annual C, N, and P contents of 11.6?±?6.9 %, 2.8?±?1.8 %, and 0.18?±?0.08 %, and phytoplankton (>20 μm) 6.8?±?6.0 %, 1.6?±?1.5 %, and 0.09?±?0.08 %, respectively. The C/N/P ratios correspond to the lowest already found to date for a marine environment. The low C contents must have been brought about by a predominance of gelatinous zooplankton, like Doliolids/ Salps and also Pteropods. Average annual nutrient concentrations in the water were 0.21?±?0.1 μM for phosphate, 0.08?±?0.1 μM for nitrite, 0.74?±?1.6 μM for nitrate, and 1.27?±?1.1 μM for ammonium. N/P ratios were around 8:1 during the first study period and 12:1 during the second. The plankton C/N/P and N/P nutrient ratios and elemental concentrations suggest that the system was oligotrophic and nitrogen limited. The sporadic intrusions of upwelling waters during the first study period had no marked effect upon the systems metabolism, likely due to dilution effects and the short residence times of water of the bay.