Uncertainty in Environmental Micropollutant Modeling.

Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.

Effects of temperature and light exposure on the decay characteristics of fecal indicators, norovirus, and Legionella in mesocosms simulating subarctic river water.

Knowledge of the decay characteristics of health-related microbes in surface waters is important for modeling the transportation of waterborne pathogens and for assessing their public health risks. Although water temperature and light exposure are major factors determining the decay characteristics of enteric microbes in surface waters, such effects have not been well studied in subarctic surface waters. This study comprehensively evaluated the effect of temperature and light on the decay characteristics of health-related microbes [Escherichia coli, enterococci, microbial source tracking markers (GenBac3 & HF183 assays), coliphages (F-specific and somatic), noroviruses GII and Legionella spp.] under simulated subarctic river water conditions. The experiments were conducted in four different laboratory settings (4 °C/dark, 15 °C/dark, 15 °C/light, and 22 °C/light). The T90 values (time required for a 90 % reduction in the population of a target) of all targets were higher under cold and dark (2.6-51.3 days depending upon targets) than under warm and light conditions (0.6-3.5 days). Under 4 °C/dark (simulated winter) water conditions, F-specific coliphages had 27.2 times higher, and coliform bacteria had 3.3 times higher T90 value than under 22 °C/light (simulated summer) water conditions. Bacterial molecular markers also displayed high variation in T90 values, with the greatest difference between 4 °C/dark and 22 °C/light recorded for HF183 DNA (20.6 times) and the lowest difference for EC23S857 RNA (6.6 times). E. coli, intestinal enterococci, and somatic coliphages were relatively more sensitive to light than water temperature, but F-specific coliphages, norovirus, and all bacterial rDNA and rRNA markers were relatively more sensitive to temperature than light exposure. Due to the slow microbial decay in winter under subarctic conditions, the microbial quality of river water might remain low for a long time after a sewage spill. This increased risk associated with fecal pollution during winter may deserve more attention, especially when river waters are used for drinking water production.

New theory of time integrative passive samplers.

Current theoretical, two compartment description of integrative passive sampling is renewed to establish a three-compartment model. The developed theoretical description includes external chemical conditions near the receiving phase, conditions inside the receiving phase and the chemically bonded compartments. New variable p, which controls the chemical bonding process into the sampler receiving phase is introduced. This new theoretical model enables derivation of equations for accumulation of masses in situations where convective mass transfer coefficient (h) and chemically bonding activity (p) are defined as a piece-wise constant functions of time. Previous two compartment model, which connects time average external concentration and accumulated mass is derived directly to the case where h and p are constants during the whole observation period. For other situations more complex equation is derived. Applicability of new equations are tested in laboratory experiments with fluctuating external chemical concentration.

Sediment diffusion method improves wastewater nitrogen removal in the receiving lake sediments.

Sediment microbes have a great potential to transform reactive N to harmless N2, thus decreasing wastewater nitrogen load into aquatic ecosystems. Here, we examined if spatial allocation of the wastewater discharge by a specially constructed sediment diffuser pipe system enhanced the microbial nitrate reduction processes. Full-scale experiments were set on two Finnish lake sites, Keuruu and Petäjävesi, and effects on the nitrate removal processes were studied using the stable isotope pairing technique. All nitrate reduction rates followed nitrate concentrations, being highest at the wastewater-influenced sampling points. Complete denitrification with N2 as an end-product was the main nitrate reduction process, indicating that the high nitrate and organic matter concentrations of wastewater did not promote nitrous oxide (N2O) production (truncated denitrification) or ammonification (dissimilatory nitrate reduction to ammonium; DNRA). Using 3D simulation, we demonstrated that the sediment diffusion method enhanced the contact time and amount of wastewater near the sediment surface especially in spring and in autumn, altering organic matter concentration and oxygen levels, and increasing the denitrification capacity of the sediment. We estimated that natural denitrification potentially removed 3-10% of discharged wastewater nitrate in the 33 ha study area of Keuruu, and the sediment diffusion method increased this areal denitrification capacity on average 45%. Overall, our results indicate that sediment diffusion method can supplement wastewater treatment plant (WWTP) nitrate removal without enhancing alternative harmful processes.

Public health and economic risk assessment of waterborne contaminants and pathogens in Finland.

This study shows that a variety of mathematical modeling techniques can be applied in a comprehensive assessment of the risks involved in drinking water production. In order to track the effects from water sources to the end consumers, we employed four models from different fields of study. First, two models of the physical environment, which track the movement of harmful substances from the sources to the water distribution. Second, a statistical quantitative microbial risk assessment (QMRA) to assess the public health risks of the consumption of such water. Finally, a regional computable general equilibrium (CGE) model to assess the economic effects of increased illnesses. In order to substantiate our analysis, we used an illustrative case of a recently built artificial recharge system in Southern Finland that provides water for a 300,000 inhabitant area. We examine the effects of various chemicals and microbes separately. Our economic calculations allow for direct effects on labor productivity due to absenteeism, increased health care expenditures and indirect effects for local businesses. We found that even a considerable risk has no notable threat to public health and thus barely measurable economic consequences. Any epidemic is likely to spread widely in the urban setting we examined, but is also going to be short-lived in both public health and economic terms. Our estimate for the ratio of total and direct effects is 1.4, which indicates the importance of general equilibrium effects. Furthermore, the total welfare loss is 2.4 times higher than the initial productivity loss. The major remaining uncertainty in the economic assessment is the indirect effects.

Contamination risk of raw drinking water caused by PFOA sources along a river reach in south-western Finland.

Transport of perfluorooctanoic acid (PFOA) was simulated in the beginning of River Kokemäenjoki in Finland using one-dimensional SOBEK river model. River Kokemäenjoki is used as a raw water source for an artificial groundwater recharge plant, and the raw water intake plant is located near the downstream end of the model application area. Measured surface water and wastewater concentrations were used to determine the PFOA input to the river and to evaluate the simulation results. The maximum computed PFOA concentrations in the river at the location of the raw water intake plant during the simulation period Dec. 1, 2011-Feb. 16, 2014 were 0.92 ng/l and 3.12 ng/l for two alternative modeling scenarios. These concentration values are 2.3% and 7.8%, respectively, of the 40 ng/l guideline threshold value for drinking water. The current annual median and maximum PFOA loads to the river were calculated to be 3.9 kg/year and 10 kg/year respectively. According to the simulation results, the PFOA load would need to rise to a level of 57 kg/year for the 40 ng/l guideline value to be exceeded in river water at the raw water intake plant during a dry season. It is thus unlikely that PFOA concentration in raw water would reach the guideline value without the appearance of new PFOA sources. The communal wastewater treatment plants in the study area caused on average 11% of the total PFOA load. This raises a concern about the origin of the remaining 89% of the PFOA load and the related risk factors.

Assimilation of satellite data to 3D hydrodynamic model of Lake Säkylän Pyhäjärvi.

To analyze the applicability of direct insertion of total suspended matter (TSM) concentration field based on turbidity derived from satellite data to numerical simulation, dispersion studies of suspended matter in Lake Säkylän Pyhäjärvi (lake area 154 km²; mean depth 5.4 m) were conducted using the 3D COHERENS simulation model. To evaluate the practicality of direct insertion, five cases with different initialization frequencies were conducted: (1) every time, when satellite data were available; (2) every 10 days; (3) 20 days; (4) 30 days; and (5) control run without repeated initialization. To determine the effectiveness of initialization frequency, three methods of comparison were used: simple spatial differences of TSM concentration without biomass in the lake surface layer; averaged spatial differences between initialization data and the forecasts; and time series of TSM concentration and observation data at 1 m depth at the deepest point of the lake. Results showed that direct insertion improves the forecast significantly, even if it is applied less often.

Finite-size effects in dynamics of zero-range processes.

The finite-size effects prominent in zero-range processes exhibiting a condensation transition are studied by using continuous-time Monte Carlo simulations. We observe that, well above the thermodynamic critical point, both static and dynamic properties display fluidlike behavior up to a density ρc(L), which is the finite-size counterpart of the critical density ρc=ρc(L→∞). We determine this density from the crossover behavior of the average size of the largest cluster. We then show that several dynamical characteristics undergo a qualitative change at this density. In particular, the size distribution of the largest cluster at the moment of relocation, the persistence properties of the largest cluster, and the correlations in its motion are studied.

Diffusion between evolving interfaces.

Diffusion in an evolving environment is studied by continuous-time Monte Carlo simulations. Diffusion is modeled by continuous-time random walkers on a lattice, in a dynamic environment provided by bubbles between two one-dimensional interfaces driven symmetrically towards each other. For one-dimensional random walkers constrained by the interfaces, the bubble size distribution dominates diffusion. For two-dimensional random walkers, it is also controlled by the topography and dynamics of the interfaces. The results of the one-dimensional case are recovered in the limit where the interfaces are strongly driven. Even with simple hard-core repulsion between the interfaces and the particles, diffusion is found to depend strongly on the details of the dynamical rules of particles close to the interfaces.

Roughness of two nonintersecting one-dimensional interfaces.

The dynamics of two spatially discrete one-dimensional single-step model interfaces with a noncrossing constraint is studied in both nonsymmetric propagating and symmetric relaxing cases. We consider possible scaling scenarios and study a few special cases by using continuous-time Monte Carlo simulations. The roughness of the interfaces is observed to be nonmonotonic as a function of time, and in the stationary state it is nonmonotonic also as a function of the strength of the effective force driving the interfaces against each other. This is related on the one hand to the reduction of the available configuration space and on the other hand to the ability of the interfaces to conform to each other.