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Kosonen H.,Aalto University | Heinonen M.,Aalto University | Mikola A.,Aalto University | Haimi H.,Aalto University | And 5 more authors.
Environmental Science and Technology | Year: 2016

The nitrous oxide emissions of the Viikinmäki wastewater treatment plant were measured in a 12 month online monitoring campaign. The measurements, which were conducted with a continuous gas analyzer, covered all of the unit operations of the advanced wastewater-treatment process. The relation between the nitrous oxide emissions and certain process parameters, such as the wastewater temperature, influent biological oxygen demand, and ammonium nitrogen load, was investigated by applying online data obtained from the process-control system at 1 min intervals. Although seasonal variations in the measured nitrous oxide emissions were remarkable, the measurement data indicated no clear relationship between these emissions and seasonal changes in the wastewater temperature. The diurnal variations of the nitrous oxide emissions did, however, strongly correlate with the alternation of the influent biological oxygen demand and ammonium nitrogen load to the aerated zones of the activated sludge process. Overall, the annual nitrous oxide emissions of 168 g/PE/year and the emission factor of 1.9% of the influent nitrogen load are in the high range of values reported in the literature but in very good agreement with the results of other long-term online monitoring campaigns implemented at full-scale wastewater-treatment plants. © 2016 American Chemical Society.

Aurela M.,Finnish Meteorological Institute | Saarikoski S.,Finnish Meteorological Institute | Niemi J.V.,Helsinki Region Environmental Services Authority | Niemi J.V.,University of Helsinki | And 6 more authors.
Aerosol and Air Quality Research | Year: 2015

Chemical characterization of non-refractory submicron particles (NR-PM1) and source apportionment of organic aerosols (OA) were carried out at four different sites in the Helsinki metropolitan area, Finland, using an Aerodyne Aerosol Chemical Speciation Monitor (ACSM). Two of the sites represented suburban residential areas, whereas the other two were traffic sites, one in a curbside in downtown and the other one in a suburban highway edge. The residential and the curbside measurements were conducted during the winter, but the highway campaign was carried out in the autumn. NR-PM1 were composed mainly of organics (40–68% in the different sites), followed by sulphate (11–34%), nitrate (12–16%), ammonium (7.8–8.5%) and chloride (0.24–1.3%). The mean concentrations of NR-PM1 were quite similar during the winter campaigns (10.1–12.5 μg/m3), but NR-PM1 was clearly lower during the autumn campaign at the highway site (6.0 μg/m3) due to the meteorology (favourable mixing conditions), small concentrations of long-range transported particles and non-intensive heating period locally and regionally. Using a multilinear engine algorithm (ME-2) and the custom software tool Source Finder (SoFI), the organic fraction was divided into two or three types of OA representing hydrocarbon-like organic aerosol (HOA), oxygenated organic aerosol (OOA), and in three sites, biomass burning organic aerosol (BBOA). At the downtown traffic site (Curbside), BBOA could not be found, probably because most of the local wood burning occurs in the suburban areas of the Helsinki region. OOA had the largest contribution to OA at all the sites (50–67%). The contribution of HOA was higher at the traffic sites (25–32%) than at the residential sites (15–18%). At the suburban residential and highway sites, the contribution of BBOA was high (25–30%). Especially during cold periods, very high BBOA contributions (~50%) were observed at the residential sites. © Taiwan Association for Aerosol Research.

Soares J.,Finnish Meteorological Institute | Kousa A.,Helsinki Region Environmental Services Authority | Kukkonen J.,Finnish Meteorological Institute | Matilainen L.,Helsinki Region Environmental Services Authority | And 10 more authors.
Geoscientific Model Development | Year: 2014

A mathematical model is presented for the determination of human exposure to ambient air pollution in an urban area; the model is a refined version of a previously developed mathematical model EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes). The model combines predicted concentrations, information on people's activities and location of the population to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments. The revisions of the modelling system containing the EXPAND model include improvements of the associated urban emission and dispersion modelling system, an improved treatment of the time use of population, and better treatment for the infiltration coefficients from outdoor to indoor air. The revised model version can also be used for estimating intake fractions for various pollutants, source categories and population subgroups. We present numerical results on annual spatial concentration, time activity and population exposures to PM2.5 in the Helsinki Metropolitan Area and Helsinki for 2008 and 2009, respectively. Approximately 60% of the total exposure occurred at home, 17% at work, 4% in traffic and 19% in other microenvironments in the Helsinki Metropolitan Area. The population exposure originating from the long-range transported background concentrations was responsible for a major fraction, 86 %, of the total exposure in Helsinki. The largest local contributors were vehicular emissions (12 %) and shipping (2 %). © Author(s) 2014. CC Attribution 3.0 License.

Wanner L.,Catalan Institution for Research and Advanced Studies | Wanner L.,University Pompeu Fabra | Rospocher M.,Fondazione Bruno Kessler | Vrochidis S.,Center for Research and Technology Hellas | And 18 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this demonstration, we present an environmental information system that addresses this demand in its full complexity in the context of the PESCaDO EU project. Specifically, we will show a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-based knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. © Springer-Verlag Berlin Heidelberg 2015.

Corona F.,Aalto University | Mulas M.,Aalto University | Haimi H.,Aalto University | Sundell L.,Helsinki Region Environmental Services Authority | And 2 more authors.
Journal of Process Control | Year: 2013

Due to stringent environmental regulations, wastewater treatment plants are always challenged to meet new constraints in terms of water pollution prevention. In such an effort, the number of sensors and data available in the plants have increased considerably during the last decades. However, the quality of the collected data and the sensor reliability are often poor mainly due to the hostile environment in which the measurement equipment has to function. In this work, we present the design of an array of soft-sensors to estimate the nitrate concentration in the post-denitrification filter unit of the Viikinmäki wastewater treatment plant in Helsinki (Finland). The developed sensors aim at supporting the existing hardware analyzers by providing a reliable back-up system in case of malfunction. The main stages of the soft-sensors' design are discussed and the development illustrated in detail, starting from the preliminary preprocessing of the available process measurements where sample and variable selection has been performed, toward the calibration of the regression models and discussion on the performance results. The estimation accuracy together with the light computational cost of the developed soft-sensors demonstrate their potential for an on-line implementation in the plant's control system. © 2012 Elsevier Ltd. All rights reserved.

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