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Long-term trends of ambient gaseous pollutants and particulate matter in Helsinki metropolitan area were analyzed from 1994 to 2019. Measurement data from ten monitoring stations located in different types of urban environments including traffic, urban background, rural background, and suburban area were included in this study. We analyzed gas-phase air pollutants, such as NO, NO2, NOx, O3, SO2 and CO; and for aerosol pollutants, we explored mass concentrations for particles smaller than 10 µm and 2.5 µm in diameter (PM10 and PM2.5, respectively ). In order to quantify trends in the data, we deployed a non-parametric Mann-Kendall test and Theil-Sen method. The results were compared with the regional emissions trends and changes in meteorological conditions. Our analysis indicates that SO2 and CO in all stations have decreased to values corresponding to their regional background concentration levels and their role as urban air pollutants have diminished. Our results from the Helsinki Metropolitan area during the last 25 years show that the air quality improved and all the air pollutant concentrations show a decreasing trend, except ozone. Based on our analysis of the Air Quality Index (AQI) at traffic and urban background environments, NO2 concentration, which have typically represented the health effects resulting from vehicular traffic, is rapidly decreasing also in traffic environments. The current AQI standard therefore lacks clarity on the potential health risks from other air pollutants emitted from traffic exhaust. In addition, the air quality indicators currently considered in the AQI do not represent well enough residential wood burning and the possible health outcomes from its exposure. We suggest that the current AQI should be revised in a way that new air quality parameters would be considered, which would better represent the health effects resulting from these local combustion sources.

Tiivistelmä. Tämän pro gradu -tutkielman tavoitteena on tarkastella koulukoiratoiminnan toteutusta sekä oppilaiden ja opettajien käsityksiä koulukoiratoiminnasta eräässä pohjoissuomalaisessa yhtenäiskoulussa. Tutkielman teoreettisessa viitekehyksessä esitellään eläinavusteisen ja koira-avusteisen toiminnan muotoja ja vaikutuksia sekä koiran roolia ja merkitystä kasvatustyössä aiemman tutkimuskirjallisuuden perusteella. Tutkimus toteutettiin fenomenografisena tapaustutkimuksena. Aineisto kerättiin tutkimuskoulun oppilailta, opettajilta ja koulukoiran ohjaajana toimivalta oppilashuollon työntekijältä teemahaastattelujen avulla. Haastatteluja toteutettiin yhteensä kymmenen, joista neljä oli oppilaiden ryhmähaastatteluja. Aineisto analysoitiin aineistolähtöisellä sisällönanalyysillä. Tutkimustulosten perusteella voidaan todeta, että sekä opettajien että oppilaiden käsitykset koulukoirasta ovat hyvin positiivisia ja koulukoira on saanut koululla myönteisen vastaanoton. Koulukoiran nähtiin parantavan oppilaan henkistä hyvinvointia, lisäävän iloa ja mielekkyyttä koulunkäyntiin, vahvistavan lapsen tunne- ja vuorovaikutustaitoja ja vähentävän häiriökäyttäytymistä. Opettajien mukaan koira luo hyväksyvää ja lämmintä tunnelmaa, mikä helpottaa vaikeistakin asioista puhumista ja siten tukee lapsen ja aikuisen välisen keskusteluyhteyden syntymistä. Koiran myös nähtiin tukevan tuntityöskentelyä, lisäävän motivaatiota sekä helpottavan erityisesti ääneen lukemista. Toisaalta erityisesti oppilaat kokivat koiran läsnäolon ja siitä johtuvan innostuksen myös jossain määrin heikentävän työrauhaa ja keskittymistä tuntitilanteissa. Tutkimuksen tulokset olivat suurimmalta osin linjassa aiemman tutkimustiedon kanssa. Tulosten perusteella voidaan päätellä, että koulukoiratoiminnan avulla on mahdollista tukea monipuolisesti oppilaan oppimista ja koulunkäyntiä sekä psyykkistä ja sosiaalista hyvinvointia. Lisäksi koulukoiran läsnäolo voi tutkimustulosten perusteella parantaa myös opettajan työhyvinvointia.

The main chemical composition of PM1 (total organics, black carbon, sulphate, nitrate and ammonium), mass concentrations of PM2.5 and PM2.5–10 and concentration of specific trace gases were measured in a high temporal resolution from May 2013 to April 2015 in the city centre of Helsinki, Finland. On average, the concentrations of PM2.5 and PM2.5–10 were 9.1 µg m–3 and 16 µg m–3, respectively, during a two-year campaign. PM1 consisted mostly of organics (60%), followed by sulphate (12%), black carbon (11%), nitrate (9.8%) and ammonium (6.5%). The particle and gas data were combined with the meteorological data in order to obtain information on how local meteorology affects concentrations of air pollutants. Two meteorological parameters that mostly affected the pollutant concentrations were the wind speed and temperature, while sulphate and PM2.5–10 were also impacted by the relative humidity. The highest concentrations of the measured PM1 components were observed when the wind was calm or the temperature was either very cold or very warm. PM2.5–10 concentrations were at the highest during calm or very windy conditions, due to local street and construction dust. The seasonal-and diurnal-varying mixing height did not seem to affect markedly the concentrations of pollutants. Overall, air quality in terms of the aerosol mass was governed by three different main pollution sources in the Helsinki city centre: 1) local sources, of which traffic-related emissions were the most important; 2) long-range or regional transport of pollutants; and 3) local sources of organic aerosol. © 2019, Finnish Environment Institute. All rights reserved.

Kandidaatintyössäni käsittelen positiivista pedagogiikkaa ja sen merkitystä oppilaan oppimisen ja hyvinvoinnin kannalta. Tutkielman tavoitteena on selvittää, miten opettaja voi hyödyntää positiivista pedagogiikkaa opetuksessaan sekä miten positiivisen pedagogiikan avulla voidaan tukea oppilaan hyvinvointia koulussa ja sen ulkopuolella. Tutkielma toteutetaan integroivana kirjallisuuskatsauksena ja sen pääpainopiste on alakoulussa ja alakouluikäisissä lapsissa. Tarkastelen työssäni positiivista pedagogiikkaa myös tukea tarvitsevan oppilaan näkökulmasta. Positiivinen pedagogiikka on positiiviseen psykologiaan pohjautuva kasvatuksellinen suuntaus, jonka tavoitteena on perinteisen opetuksen ja koulutyön ohella edistää lapsen hyvinvointia ja henkilökohtaista kasvua sekä ennaltaehkäistä ongelmia. Positiivinen pedagogiikka keskittyy oppilaan vahvuuksiin ja voimavaroihin, myönteisiin tunteisiin, koulunkäynnin merkityksellisyyteen sekä luokan myönteiseen sosiaaliseen vuorovaikutukseen ja yhteisöllisyyteen. Positiivista pedagogiikkaa on mahdollista toteuttaa sekä eksplisiittisesti opetettuna, kuten erillisten oppituntien muodossa, että implisiittisesti eri oppiaineisiin ja luokan jokapäiväiseen toimintaan integroituna. Positiivisen pedagogiikan on todettu vahvistavan oppilaan kouluviihtyvyyttä ja hyvinvointia, mikä puolestaan tukee keskittymistä, osallisuutta ja luovaa ajattelua sekä parantaa lapsen itseluottamusta ja yleistä koulumenestystä. Oppilaan hyvinvointia voidaan edistää koulussa muun muassa myönteisiä tunteita vahvistamalla, sitoutumista ja koulutyön merkityksellisyyttä lisäämällä sekä oppilaiden välisiin suhteisiin ja opettaja-oppilassuhteeseen panostamalla. Myös menestymisen ja onnistumisen hetket sekä kokemukset pätevyydestä ja autonomiasta lisäävät hyvinvointia ja koulutyytyväisyyttä. Lapsen myönteisen minäkuvan kehittymistä ja hyvinvoinnin rakentumista voidaan tukea myös vahvuusperustaisen opetuksen keinoin. Erityisesti tukea tarvitsevan oppilaan itsetunnon, hyvinvoinnin ja oppimisen tukemisessa positiivisella pedagogiikalla ja vahvuusperustaisella opetuksella on merkittävä rooli.

Reliable and self-consistent data on air quality are needed for an extensive period of time for conducting long-term, or even lifetime health impact assessments. We have modelled the urban-scale concentrations of fine particulate matter (PM₂.₅) in the Helsinki Metropolitan Area for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The high-resolution urban computations included both the emissions originated from vehicular traffic (separately exhaust and suspension emissions) and those from small-scale combustion, and were conducted using the road network dispersion model CAR-FMI and the multiple-source Gaussian dispersion model UDM-FMI. The modelled concentrations of PM2.5 agreed fairly well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. The modelled concentration trends were also evaluated for earlier years, until 1988, using proxy analyses. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980s and 1990s), compared with the results for more recent years (2000s and early 2010s). The local vehicular emissions were about 5 times higher in the 1980s, compared with the emissions during the latest considered years. The local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM₂.₅ occurred in the 1980s; these have since decreased to about to a half of the highest values. In general, regional background was the largest contribution in this area. Vehicular exhaust has been the most important local source, but the relative shares of both small-scale combustion and vehicular non-exhaust emissions have increased in time. The study has provided long-term, high-resolution concentration databases on regional and urban scales that can be used for the assessment of health effects associated with air pollution.

We refined an urban-scale dispersion modelling system by adding a road dust suspension model, FORE. The deterministic modelling includes both vehicular exhaust emissions (including cold start and cold driving) and suspended road dust. The urban scale modelling system was used in combination with the regional scale chemical transport model LOTOS- EUROS, for 2008, and the measured regional background concentrations, for 2010. The predictions were compared against measured concentrations of PM 2.5 and PM 10 . PM 2.5 concentrations were slightly and the PM 10 concentrations substantially under-predicted in 2008, mainly due to the under-predicted regional background concentration. Source contri- butions of suspended road dust varied from 2% to 8% and from 12% to 38% for PM 2.5 and PM 10 , respectively. Long-range transported contributions at the urban traffic stations were 72% to 92% for PM 2.5 and 50% to 83% for PM 10 .

Background: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. Objectives: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. Methods: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. Results: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. Conclusions: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5. © 2014 Elsevier Ltd.

We have analysed the chemosensitivity of 121 human melanoma metastases in the 6-day mouse subrenal capsule assay (SRCA). A total of 13 different chemotherapy regimens were analysed in four successive series. By the original criteria of the assay, 75% of tumours were sensitive to at least one regimen. The most effective combinations, giving sensitivity rates > 40%, were of dacarbazine, vincristine, and BCNU (DOB) with or without metronidazole, followed by cisplatin plus etoposide (Plat-VP16) combined with interferon. On the basis of the SRCA screening we developed a four-drug chemotherapy regimen combined with interferon for metastatic melanoma. Retrospectively analysed the assay correctly identified 77% of clinical responders and 54% of clinical non-responders. The overall predictive accuracy was 65%. Despite the limitations of the assay, it can be used for screening new drugs or combinations.

The prognostic value of flow cytometric parameters and tumour growth rate of melanoma metastases under the mouse renal capsule was investigated for tumours from 117 consecutive patients referred to the Helsinki University Central Hospital Melanoma Team. DNA flow cytometry (FCM) was interpretable for the tumours of 114 patients, and growth rate analysis for 82 patients, both results being available from 79 patients. Thirty-six percent of the tumours were DNA diploid and 64% DNA aneuploid. Tumour ploidy and S-phase fraction were shown by multivariate Cox model analysis to be independent prognostic variables and major determinants of survival after first recurrence. Patients with DNA diploid or aneuploid tumours survived a median 16 and 27 months, respectively. A high growth rate of tumour sample in vivo under the mouse renal capsule tended to be a sign of poor prognosis, although not reaching statistical significance. Combining the results of FCM, tumour growth rate and TNM stage, we propose a highly efficient prognostic scoring method. Patients with a score above 0.75 had a median survival of 11 months compared to 30 months among patients scoring under 0.75 (P less than 0.0001). This score was the most significant (P less than 0.0001) prognostic factor in the Cox model when TNM stage, age, ploidy, SPF, and tumour growth rate were analysed as covariates.