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.     

Biogeochemistry of the Penobscot River watershed, Maine, USA: nutrient export patterns for carbon, nitrogen, and phosphorus

Watershed exports of carbon, nitrogen, phosphorus, major solutes, and suspended sediments were examined during five water years in the Penobscot River basin, which forms part of the Gulf of Maine watershed. Mean annual exports of dissolved organic carbon (DOC) in the Penobscot River were 58 kg C ha(-1) year(-1), whereas cumulative yearly watershed flux of DOC during the study period ranged from 8.6 to 16.1 × 10(10) g C year(-1) and averaged 11.7 × 10(10) g C year(-1). Watershed exports of total soluble N (TN) and total soluble P in the Penobscot River averaged 1.9 and 0.02 kg ha(-1) year(-1), respectively. Companion studies in two other major Maine rivers indicated that mean annual exports of DOC and TN in the Androscoggin River were 40 kg C ha(-1) year(-1) and 2.0 kg N ha(-1) year(-1), whereas exports in the Kennebec River were 43 kg C ha(-1) year(-1) and 2.2 kg N ha(-1) year(-1). Extrapolation of results from this investigation and a previous complementary study indicates that estuaries and coastal waters in the Gulf of Maine receive at least 1.0 × 10(10) g N year(-1) and 2.5 × 10(11) g C year(-1) in combined runoff from the four largest Maine river basins. Soluble exports of Ca + Mg + Na minus wet deposition inputs of cations in the Penobscot system were approximately 1,840 mol(c) ha(-1) year(-1), which represents a minimum estimate of cation denudation from the watershed. Based on its low N and P export rates, the Penobscot River watershed represents an example of reference conditions for use as a benchmark in ecological assessments of river water quality restoration or impairment. In addition, the biogeochemical metrics from this study provide an historical baseline for analysis of future trends in nutrient exports from the Penobscot watershed as a function of changing climatic and land use patterns.