Determining the ventilation and aerosol deposition rates from routine indoor-air measurements

Measurement of air exchange rate provides critical information in energy and indoor-air quality studies. Continuous measurement of ventilation rates is a rather costly exercise and requires specific instrumentation. In this work, an alternative methodology is proposed and tested, where the air exchange rate is calculated by utilizing indoor and outdoor routine measurements of a common pollutant such as SO₂, whereas the uncertainties induced in the calculations are analytically determined. The application of this methodology is demonstrated, for three residential microenvironments in Athens, Greece, and the results are also compared against ventilation rates calculated from differential pressure measurements. The calculated time resolved ventilation rates were applied to the mass balance equation to estimate the particle loss rate which was found to agree with literature values at an average of 0.50 h^?1. The proposed method was further evaluated by applying a mass balance numerical model for the calculation of the indoor aerosol number concentrations, using the previously calculated ventilation rate, the outdoor measured number concentrations and the particle loss rates as input values. The model results for the indoors’ concentrations were found to be compared well with the experimentally measured values.     

Co-occurrence profiles of trace elements in potable water systems: a case study

Potable water samples (N?=?74) from 19 zip code locations in a region of Greece were profiled for 13 trace elements composition using inductively coupled plasma mass spectrometry. The primary objective was to monitor the drinking water quality, while the primary focus was to find novel associations in trace elements occurrence that may further shed light on common links in their occurrence and fate in the pipe scales and corrosion products observed in urban drinking water distribution systems. Except for arsenic at two locations and in six samples, rest of the analyzed elements was below maximum contaminant levels, for which regulatory values are available. Further, we attempted to hierarchically cluster trace elements based on their covariances resulting in two groups; one with arsenic, antimony, zinc, cadmium, and copper and the second with the rest of the elements. The grouping trends were partially explained by elements’ similar chemical activities in water, underscoring their potential for co-accumulation and co-mobilization phenomena from pipe scales into finished water. Profiling patterns of trace elements in finished water could be indicative of their load on pipe scales and corrosion products, with a corresponding risk of episodic contaminant release. Speculation was made on the role of disinfectants and disinfection byproducts in mobilizing chemically similar trace elements of human health interest from pipe scales to tap water. It is warranted that further studies may eventually prove useful to water regulators from incorporating the acquired knowledge in the drinking water safety plans.     

Complaints regarding occupational health and safety in the area of Thessaloniki (Greece)

This paper deals with the complaints regarding occupational health and safety in the area of Thessaloniki (Greece). It seeks to examine who are the complainers and which companies or activities do they complain about, to what extent are complaints relevant to occupational health and safety and whether the complaints result to penalties. Such complaints do not pertain to all economic sectors equally, nor do they derive from all kinds of sources. Furthermore, it appears that complaints “irrelevant” to occupational health and safety are twice as many as the “relevant” ones. In three quarters of the cases, the complaints do not result to any kind of sanction; more specifically, in one fifth of the cases, the complaints are not followed by any action taken by the office in charge. All things considered, it seems that filing complaints does not imply finding the most dangerous workplaces. Therefore, investigation of complaints should not be considered more significant than other inspection activities.     

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.     

Effective waste management with emphasis on circular economy

Environmental Science and Pollution Research -     

Consumer safety evaluation after monitoring of endocrine disruptor pesticide residues: a case study of Thessaly,Central Greece

Abstract

A large number of pesticides used in agricultural practice has been characterized or suspected to be Endocrine Disruptors, with dietary intake to be the major route of consumer’s exposure. In the present study the safety to consumer after the consumption of pome fruits and fruiting vegetables originated from the area of Thessaly, Central Greece was assessed. Objective samplings of 110 samples were conducted in local retail markets in an effort to quantify potential residues of targeted pesticides, previously characterized as endocrine disruptors. For the determination of residues, a QuEChERs-based multiresidue method coupled with liquid and gas chromatographic systems was applied and fully validated in accordance with the European Unions’s requirements in apples and tomatoes in three fortification levels. Acceptable results were obtained for all the validation parameters studied and the limit of quantification of 0.01?mg/kg and limit of detection of 0.0033?mg/kg were achieved. Based on the determined results, the most frequently determined pesticide was chlorpyrifos with a maximum concentration of 0.32?mg/kg in tomato samples. Cypermethrin and dimethoate were also determined in tomatoes and aubergines. The EFSA PRIMo rev.3.1 model was applied for all positive samples and acute risk for consumers was confirmed only in the case of determination of chlorpyrifos in tomatoes.     

Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source

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.     

Are savannas patch-dynamic systems? A landscape model

Savannas are ecosystems characterized by the coexistence of woody species (trees and bushes) and grasses. Given that savanna characteristics are mainly formed from competition, herbivory, fire, woodcutting, and patchy soil and precipitation characteristics, we propose a spatially explicit model to examine the effects of the above-mentioned parameters on savanna vegetation dynamics in space and time. Furthermore, we investigate the effects of the above-mentioned parameters on tree–bush–grass ratios, as well as the degrees of aggregation of tree–bush–grass biomass. We parameterized our model for an arid savanna with shallow soil depth as well as a mesic one with generally deeper and more variable soil depths. Our model was able to reproduce savanna vegetation characteristics for periods of time over 2000 years with daily updated time steps. According to our results, tree biomass was higher than bush biomass in the arid savanna but bush biomass exceeded tree and grass biomass in the simulated mesic savanna. Woody biomass increased in our simulations when the soil's porosity values were increased (mesic savanna), in combination with higher precipitation. Savanna vegetation varied from open savanna to woodland and back to open savanna again. Vegetation cycles varied over ∼300-year cycles in the arid and ∼220-year cycles in the mesic-simulated savanna. Autocorrelation values indicated that there are both temporal and spatial vegetation cycles. Our model indicated cycling savanna vegetation at the landscape scale, cycles in cells, and patchiness, i.e. patch dynamics.     

Metabolic and molecular stress responses of the gilthead seabream Sparus aurata during long-term exposure to increasing temperatures

Tolerance to a changing climate regime and persistence in the natural environment depends on the limited capacity to acclimate to changing temperatures. The present study aimed to identify and characterize thermal limits of the Mediterranean fish Sparus aurata as well as the processes providing heat protection during exposure to high temperatures. Processes studied included heat shock protein expression, protein kinase activity and metabolic adjustments. Molecular responses were addressed through the expression of Hsp70 and Hsp90 and the phosphorylation of stress-activated protein kinases, p38 mitogen-activated protein kinase (p38 MAPK) and cJun-N-terminal kinases (JNKs). Thermal impacts on metabolic capacities were assessed by studying the maximum activities of citrate synthase (CS), malate dehydrogenase (MDH) and 3-hydroxyacyl CoA dehydrogenase (HOAD) as well as pyruvate kinase (PK) and lactate dehydrogenase (L-LDH). The expression of Hsp70 and hsp90 was activated when the fish were exposed to temperatures beyond 20°C. Increased phosphorylation of p38 MAPK and JNKs indicated the parallel activation of MAPK signaling cascades and the potential involvement of MAPKs in the induction of Hsp genes. Exposure to extreme temperatures beyond 24°C caused an increase in the enzymatic activity of PK and LDH indicating an enhanced glycolytic potential.