Effect of point source removal on mercury bioaccumulation in an industrial pond

A one hectare pond on the headwaters of a mercury-contaminated creek in Oak Ridge, Tennessee acted as a biochemical reactor for the production of methylmercury, increasing waterborne methylmercury concentrations in the stream below the pond discharge. The flow of the creek was diverted around the pond in order to eliminate this input. Waterborne total mercury, methylmercury, and mercury in fish, were monitored in the pond and stream before and after bypass. Waterborne methylmercury concentration in the creek downstream from the pond decreased over 800% following diversion of streamflow around the pond, but mercury in redbreast sunfish in the pond tailwater did not decline similarly. Within the pond, now isolated from fresh waterborne mercury inputs from the stream, methylmercury concentrations in the water column remained similar to levels present before bypass. However, mercury concentrations in sunfish in the pond decreased approximately 75% following bypass, despite the continued presence of highly contaminated sediments (approximately 50 mg Hg/kg dry weight). We concluded that a decrease in the fraction of 'dissolved methylmercury' in the isolated pond relative to pre-bypass conditions explained the decrease in mercury in fish within the pond. That observation also indicates that mercury associated with pond sediments was relatively unavailable for eventual bioaccumulation when compared to 'fresh' mercury contributed by upstream sources. The lack of a post-bypass decrease in mercury concentrations in tailwater fish was also likely to be associated with the particle-associated nature of waterborne methylmercury exported from the pond.     

Forest biomass accumulation is an important source of acidity to forest soils: Data from Swedish inventories of forests and soils 1955 to 2010

The input of acidity to Swedish forest soils through forestry between 1955 and 2010 is compared with the acid input from atmospheric deposition. Depending on region, input of acidity from forestry was the minor part (25–45%) of the study period’s accumulated acid input but is now the dominating source (140–270 mol_c ha⁻¹ year⁻¹). The net uptake of cations due to the increase in standing forest biomass, ranged between 35 and 45% of the forestry related input of acidity while whole-tree harvesting, introduced in the late 1990s, contributed only marginally (< 2%). The geographical gradient in acid input is reflected in the proportion of acidified soils in Sweden but edaphic properties contribute to variations in acidification sensitivity. It is important to consider the acid input due to increases in standing forest biomass in acidification assessments since it is long-term and quantitatively important.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13280-021-01540-y.     

Ambient concentrations and source apportionment of PCBs and trace elements around an industrial area in Izmir, Turkey

Atmospheric concentrations of polychlorinated biphenyls (PCBs) and trace elements were measured at two sites (Industrial and Urban) located around the Aliaga industrial region, Izmir, Turkey. Average sigma 36PCB concentrations were 3136+/-824 and 1371+/-642 pg m(-3) for summer and winter periods, respectively in the Industrial site and they were 314+/-129 and 847+/-610 pg m(-3) in the Urban site. The elemental content showed that the PM(10) measured at the Industrial site was dominated by terrestrial elements and trace elements emitted by the iron-steel plants (Fe, Zn and Pb). The elemental profile at the Urban site was typical for Aegean Region that was dominated by terrestrial elements (Ca, Al, Mg) and sea salt (Na). Sources of particle-phase PCBs and trace elements were identified using factor analysis (FA) and were apportioned by chemical mass balance (CMB) model. FA suggested that the steel industry, fuel oil combustion, or the nearby vinyl chloride process in the petrochemical plant, and soil were significant PCB sources. CMB results showed that at the Industrial site, the contribution of steel industry and soil to particle-phase PCBs were 71% and 22%, respectively, while at the Urban site, the contributions were 33% and 49%, respectively. Steel industry was also the dominant contributor for trace elements around the site. Fugacity calculations in air and soil showed that the soil acts as a secondary source to the atmosphere for low molecular weight PCBs especially in summer and as a sink for the higher molecular weight ones.     

A design of two simple models to predict PCDD/F concentrations in vegetation and soils

The vegetation and soil levels of the 17 polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/F) toxic congeners were calculated by means of a vegetation and a soil model, respectively. Both models predicted the levels of the 17 PCDD/F congeners in quite good agreement with the observed results although the soil model was more accurate than the vegetation model. Four different pathways of contribution to the vegetation concentrations were taken into account: vapour-phase absorption, dry particle deposition, wet particle deposition and uptake by root. The most important pathway was the vapour-phase absorption and the less was the uptake by root. In the soils model four pathways were considered: background soil concentration, dry particle deposition, wet particle deposition and uptake by root. After the background concentration, the most important pathway was the wet deposition.     

Evaluation of land-use regression models used to predict air quality concentrations in an urban area

Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM_2.5, NO_x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.     

Combining Neural Network Models to Predict Spatial Patterns of Airborne Pollutant Accumulation in Soils around an Industrial Point Emission Source

Abstract

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R² values were found to be higher than 0.9 while, for the contributing individual networks, values for R² ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.     

Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan

This study identifies the natural background, anthropogenic background and distribution of contamination caused by heavy metal pollutants in soil in Chunghua County of central Taiwan by using a finite mixture distribution model (FMDM). The probabilities of contaminated area distribution are mapped using single-variable indicator kriging and multiple-variable indicator kriging (MVIK) with the FMDM cut-off values and regulation thresholds for heavy metals. FMDM results indicate that Cr, Cu, Ni and Zn can be individually fitted by a mixture model representing the background and contamination distributions of the four metals in soil. The FMDM cut-off values for contamination caused by the metals are close to the regulation thresholds, except for the cut-off value of Zn. The receiver operating characteristic (ROC) curve validates that indicator kriging and MVIK with FMDM cut-off values can reliably delineate heavy metals contamination, particularly for areas lacking background information and high heavy metal concentrations in soil.