Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

Biomass-based carbon capture and utilization in kraft pulp mills

Corporate image, European Emission Trading System and Environmental Regulations, encourage pulp industry to reduce carbon dioxide (CO₂) emissions. Kraft pulp mills produce CO₂ mainly in combustion processes. The largest sources are the recovery boiler, the biomass boiler, and the lime kiln. Due to utilizing mostly biomass-based fuels, the CO₂ is largely biogenic. Capture and storage of CO₂ (CCS) could offer pulp and paper industry the possibility to act as site for negative CO₂ emissions. In addition, captured biogenic CO₂ can be used as a raw material for bioproducts. Possibilities for CO₂ utilization include tall oil manufacturing, lignin extraction, and production of precipitated calcium carbonate (PCC), depending on local conditions and mill-specific details. In this study, total biomass-based CO₂ capture and storage potential (BECCS) and potential to implement capture and utilization of biomass-based CO₂ (BECCU) in kraft pulp mills were estimated by analyzing the impacts of the processes on the operation of two modern reference mills, a Nordic softwood kraft pulp mill with integrated paper production and a Southern eucalyptus kraft pulp mill. CO₂ capture is energy-intensive, and thus the effects on the energy balances of the mills were estimated. When papermaking is integrated in the mill operations, energy adequacy can be a limiting factor for carbon capture implementation. Global carbon capture potential was estimated based on pulp production data. Kraft pulp mills have notable CO₂ capture potential, while the on-site utilization potential using currently available technologies is lower. The future of these processes depends on technology development, desire to reuse CO₂, and prospective changes in legislation.

     

A real-time monitoring system for Kerava river quality

The hygienic quality of the water of the Kerava river, southern Finland, deteriorates occasionally. The purpose of the study was to design a real-time monitoring system that would inform the public using the river for recreational purposes about the changes in water quality. The system was constrained to consist of on-line sensing of water quality and quantity, and adjacent forecasting models. Four different system alternatives were analyzed and compared. The first alternative observes river flow in real-time; the second alternative also monitors water temperature, turbidity, pH, conductivity and dissolved oxygen. The data collected in this way are used to forecast Streptococcus and E. coli concentrations, using canonical correlation and regression analysis. The third configuration is a two-step procedure, where river flow is first predicted by an ARMAX model and the hygienic state is then based on the flow estimate, as in the first assemblage. The most expensive monitoring system, which at present is the least well-known, is to apply the Lidar system, where the hygienic status of the river quality is observed directly using laser technology, placing less emphasis on modeling. In this paper, the alternatives are formulated and a preliminary comparison is made, using the criteria of operational feasibility, prediction uncertainty, investment and maintenance costs, and suitability for in-situ monitoring.