We have developed a local mass balance model for the St. Lawrence River near Cornwall, Ontario that describes the fate and transport of mercury in three forms, elemental, divalent, and methylated, in a five compartment environment (air, water, sediments, periphyton, and benthos). Our objective was to construct a steady-state mass balance model to determine the dominant sources and sinks of mercury in this environment. We compiled mercury concentrations, fluxes, and transformation rates from previous studies completed in this section of the river to develop the model. The inflow of mercury was the major source to this system, accounting for 0.42 mol month−1, or 95.5% of all mercury inputs, whereas outflow was 0.28 mol month−1, or 63.6% of all losses, and sediment deposition was 0.12 mol month−1, or 27.3% of all losses. Uncertainty estimates were greatest for advective fluxes in surface water, porewater, periphyton, and benthic invertebrates.

Determination of 14C and levoglucosan can provide insights into the quantification of source contributions to carbonaceous aerosols, yet there is still uncertainty on the partitioning of organic carbon (OC) into biomass burning OC (OCbb) and biogenic emission OC (OCbio). Carbonaceous species, levoglucosan and 14C in PM2.5 were measured at three types of site in southeast China combined with Latin hypercube sampling, with the objectives to study source contributions to total carbon (TC) and their uncertainties, and to evaluate the influence of levoglucosan/OCbb ratios on OCbb and OCbio partitioning. It was found reliably that fossil fuel combustion is the main contributor (62.90–72.23%) to TC at urban and peri-urban sites. Biogenic emissions have important contribution (winter, 52.98%; summer, 45.71%) to TC at background site. With the increase in levoglucosan/OCbb ratios, the contribution of OCbio is increased while OCbb is decreased in a pattern of approximate natural logarithm at a given range.

Vegetation is often quoted as an effective measure to mitigate urban air quality problems. In this work we demonstrate by the use of computer models that the air quality effect of urban vegetation is more complex than implied by such general assumptions. By modelling a variety of real-life examples we show that roadside urban vegetation rather leads to increased pollutant concentrations than it improves the air quality, at least locally. This can be explained by the fact that trees and other types of vegetation reduce the ventilation that is responsible for diluting the traffic emitted pollutants. This aerodynamic effect is shown to be much stronger than the pollutant removal capacity of vegetation. Although the modelling results may be subject to a certain level of uncertainty, our results strongly indicate that the use of urban vegetation for alleviating a local air pollution hotspot is not expected to be a viable solution.

The accumulation of 134Cs and 85Sr within different parts of spring oilseed rape and spring wheat plants was investigated, with a particular focus on transfer to seeds after artificial wet deposition at different growth stages during a two-year field trial. In general, the accumulation of radionuclides in plant parts increased when deposition was closer to harvest. The seed of spring oilseed rape had lower concentrations of 85Sr than spring wheat grain. The plants accumulated more 134Cs than 85Sr. We conclude that radionuclides can be transferred into human food chain at all growing stages, especially at the later stages. The variation in transfer factors during the investigation, and in comparison to previous results, implies the estimation of the risk for possible transfer of radionuclides to seeds in the event of future fallout during a growing season is still subject to considerable uncertainty.

Marine water quality monitoring is performed for compliance with regulatory issues, trend detection, model validation and assessment of the effectiveness of adopted policies. As the end users are managers and policy makers, the objectives should be of practical interest and the answers should reduce the uncertainty concerning environmental impact, supporting planning and decision making. Simple and clearcut answers on environmental issues require synthesis of the field information using statistics, simulation models and multiple criteria analysis (MCA). Statistics is easy to apply whereas simulation models enable researchers to forecast future trends as well as test different scenarios. MCA allows the co-estimation of socio-economic variables providing a compromise between scientists’ and policy makers’ priorities. In addition, stakeholders and the public have the right to know and participate. This article reviews marine water quality monitoring principles, design and data analysis procedures. A brief review of international conventions of regional seas is also included.

Major incidents involving mine waste facilities and poor environmental management practices have left the legacy of thousands of contaminated sites like in the historic mining areas in the Carpathian Basin. Associated environmental risks have triggered the development of new EU environmental legislation to prevent and minimize the effects of such incidents. The Mine Waste Directive requires the risk-based inventory of all mine waste sites in Europe by May 2012. In order to address the mining environmental problems, a standard risk-based pre-selection protocol has been developed by the EU Commission. The protocol consists of 18 simple questions about contamination source, pathway and receptor. This paper evaluates the protocol by applying it to real-life cases, adopting it to local conditions, comparing to the similar method of the European Environmental Agency standard Preliminary Risk Assessment Model (PRAMS) and by carrying out uncertainty analysis. All together, 145 ore mine waste sites have been selected for scientific testing and evaluation using the EU Mining Waste Directive (MWD) Pre-selection Protocol as a case study from Hungary. The proportion of uncertain responses to questions in the protocol for the mine waste site gives an insight of specific and overall uncertainty in the data used. Questions of the EU MWD Pre-selection Protocol are linked to a GIS system, and key parameters such as the topographic slope and distance to the nearest surface and groundwater bodies to settlements and protected areas are calculated and statistically evaluated in order to adjust the RA models to local conditions in Hungary. Results show that the adjustment of threshold values to local conditions is necessary; however, the EU MWD Pre-selection Protocol is robust and is relatively insensitive to threshold values. Results of the EU MWD Pre-selection Protocol are consistent with the pre-screening European Environmental Agency PRAMS model which further confirms that the Protocol delivers reliable selection results that are not sensitive to the selected parameters. An interesting outcome of the study is that the highest uncertainty is associated with the engineering conditions of the waste facilities, such as the heights and size.

Two methods to measure mercury concentration in soil are compared, and their compliance with international standards is determined: cold vapour atomic absorption spectrometry and thermal decomposition, amalgamation and atomic absorption spectrophotometry. The detection limit, quantification limit and uncertainty of these two analytical methods were evaluated and compared. The results indicated that thermal decomposition, amalgamation and atomic absorption spectrophotometry had a lower quantification limit and uncertainty than cold vapour atomic absorption spectrometry (quantification limit, 0.27 vs. 0.63 mg kg⁻¹; expanded uncertainty, 9.30 % vs. 10.8 %, respectively). Thermal decomposition, amalgamation and atomic absorption spectrophotometry allowed the determination of the base values for the concentration of mercury in soil recommended by international standards, achieving a lower detection limit than cold vapour atomic absorption spectrometry under the study conditions. In addition, thermal decomposition, amalgamation and atomic absorption spectrophotometry represent a more environmentally friendly alternative for mercury determination because this method uses fewer reagents and therefore generates less waste.

Since the publication of the first version of European standard EN-1948 in 1996, long-term sampling equipment has been improved to a high standard for the sampling and analysis of polychlorodibenzo-p-dioxin (PCDD)/polychlorodibenzofuran (PCDF) emissions from industrial sources. The current automated PCDD/PCDF sampling systems enable to extend the measurement time from 6-8 h to 15-30 days in order to have data values better representative of the real pollutant emission of the plant in the long period. EN-1948:2006 is still the European technical reference standard for the determination of PCDD/PCDF from stationary source emissions. In this paper, a methodology to estimate the measurement uncertainty of long-term automated sampling is presented. The methodology has been tested on a set of high concentration sampling data resulting from a specific experience; it is proposed with the intent that it is to be applied on further similar studies and generalized. A comparison between short-term sampling data resulting from manual and automated parallel measurements has been considered also in order to verify the feasibility and usefulness of automated systems and to establish correlations between results of the two methods to use a manual method for calibration of automatic long-term one. The uncertainty components of the manual method are analyzed, following the requirements of EN-1948-3:2006, allowing to have a preliminary evaluation of the corresponding uncertainty components of the automated system. Then, a comparison between experimental data coming from parallel sampling campaigns carried out in short- and long-term sampling periods is realized. Long-term sampling is more reliable to monitor PCDD/PCDF emissions than occasional short-term sampling. Automated sampling systems can assure very useful emission data both in short and long sampling periods. Despite this, due to the different application of the long-term sampling systems, the automated results could not be directly compared with manual results, not even in terms of measurement uncertainty. This investigation focuses on both uncertainty and repeatability of the automated sampling method. The standard 20988, developed by Internarional Organization of Standardization (ISO) can be used to estimate the measurement uncertainty. The results confirm that the uncertainties of manual and automated methods are comparable. At the same time, it is not appropriate to consider the manual method as a reference for the evaluation of the uncertainty of the automated sampling system, due to the high variability of both systems.

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R (2) of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.

In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH⁴⁺–N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH⁴⁺–N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing–refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH⁴⁺–N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering “real” data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.