Laskennallisia menetelmiä liittyen pienhiukkasten muodostumiseen ja kasvuun ilmakehässä

Aerosols impact the planet and our daily lives through various effects, perhaps most notably those related to their climatic and health-related consequences. While there are several primary particle sources, secondary new particle formation from precursor vapors is also known to be a frequent, global phenomenon. Nevertheless, the formation mechanism of new particles, as well as the vapors participating in the process, remain a mystery. This thesis consists of studies on new particle formation specifically from the point of view of numerical modeling. A dependence of formation rate of 3 nm particles on the sulphuric acid concentration to the power of 1-2 has been observed. This suggests nucleation mechanism to be of first or second order with respect to the sulphuric acid concentration, in other words the mechanisms based on activation or kinetic collision of clusters. However, model studies have had difficulties in replicating the small exponents observed in nature. The work done in this thesis indicates that the exponents may be lowered by the participation of a co-condensing (and potentially nucleating) low-volatility organic vapor, or by increasing the assumed size of the critical clusters. On the other hand, the presented new and more accurate method for determining the exponent indicates high diurnal variability. Additionally, these studies included several semi-empirical nucleation rate parameterizations as well as a detailed investigation of the analysis used to determine the apparent particle formation rate. Due to their high proportion of the earth's surface area, oceans could potentially prove to be climatically significant sources of secondary particles. In the lack of marine observation data, new particle formation events in a coastal region were parameterized and studied. Since the formation mechanism is believed to be similar, the new parameterization was applied in a marine scenario. The work showed that marine CCN production is feasible in the presence of additional vapors contributing to particle growth. Finally, a new method to estimate concentrations of condensing organics was developed. The algorithm utilizes a Markov chain Monte Carlo method to determine the required combination of vapor concentrations by comparing a measured particle size distribution with one from an aerosol dynamics process model. The evaluation indicated excellent agreement against model data, and initial results with field data appear sound as well.Pienhiukkaset vaikuttavat monin tavoin maapalloon ja jokapäiväiseen elämäämme. Ne voivat esimerkiksi toimia pilvien tiivistymisytimiä, jolloin ne epäsuorasti viilentävät ilmastoa. Hengitettyinä pienhiukkaset voivat koostumuksestaan ja koostaan riippuen aiheuttaa vakaviakin oireita, mikä on Suomessakin havaittu erityisesti Venäjän metsäpalojen aikana. Pienhiukkaset ovat siis varsin luonnollinen ilmiö, vaikka ihminenkin toki pystyy niitä toiminnallaan tuottamaan. Luonnostaan hiukkasmuotoisen aineen (esimerkiksi hiekkamyrskyn) lisäksi pienhiukkasten muodostumista ympäröivistä kaasuista tiivistymällä on havaittu ympäri maailmaa. Muodostustapahtumaan liittyvien mekanismien tuntemus on kuitenkin yhä puutteellista. Lukuisat havainnot viittaavat kaasumaisen rikkihapon tärkeyteen, mutta toisaalta rikkihappoa ei kaikkialla ole tarpeeksi. Siksi on esitetty, että esimerkiksi pohjoisissa havumetsissä monimuotoiset mitattavat orgaaniset höyryt osallistuisivat joko hiukkasmuodostukseen tai ainakin hiukkasten varhaiseen kasvuun. Merialueilla puolestaan levistä ja planktonkasvustosta vapautuvat jodikaasut saattavat osoittautua tärkeiksi. Näiden hypoteesien kokeellista varmentamista on toistaiseksi vaikeuttanut kyseisten höyryjen mittaamista hankaloittavat fysikaaliset ominaisuudet. Tässä väitöskirjatyössä on tutkittu hiukkasmuodostusta ja vastamuodostuneiden hiukkasten kasvua laskennallisista lähtökohdista. Tutkimuksessa on käytetty kaikkia oleellisia fysikaalisia prosesseja kuvaavaa 0-ulotteista tietokonemallia, jonka kehitys on tässä työssä ollut suuressa osassa. Muita merkittäviä osa-alueita ovat olleet aineiston käsittelyyn tarvittavien menetelmien kehitys, erilaisten herkkyystarkastelujen suorittaminen sekä tietenkin malliajojen tuottaman aineiston tulkinta. Tässä työssä tehtyjä parametrisaatioita käyttäen tietokonemalli pystyy kuvaamaan luonnossa havaittuja hiukkasmuodostustapahtumia niin pohjoisten havumetsien, rannikkoalueiden kuin myös avomeren olosuhteissa. Hiukkasten muodostumisnopeuden määrittämistä on kehitetty ja ympäröivien olosuhteiden vaikutusta hiukkasmuodostustapahtumaan tarkasteltu. Lisäksi työssä kuvataan uusi laskennallinen menetelmä hiukkasia kasvattavien höyryjen määrittämiseksi

Simulating Marine New Particle Formation and Growth Using the M7 Modal Aerosol Dynamics Modal

Peer reviewe

Spatial and Temporal Investigation of Dew Potential based on Long-Term Model Simulations in Iran

Since water shortage has been a serious challenge in Iran, long-term investigations of alternative water resources are vital. In this study, we performed long-term (1979–2018) model simulation at seven locations (costal, desert, mountain, and urban conditions) in Iran to investigate temporal and spatial variation of dew formation. The model was developed to simulate the dew formation (water and ice) based on the heat and mass balance equation with ECMWF-ERA-Interim (European Centre for Medium-Range Weather Forecasts–Re-Analysis) meteorological data as input. According to the model simulation, the maximum mean yearly cumulative dew yield (~65 L/m2) was observed in the mountain region in the north part of Iran with a yearly mean cumulative dew yield was ~36 L/m2. The dew yield showed a clear seasonal variation at all selected locations with maximum yields in winter (mean monthly cumulative 3–8 L/m2 depending on the location). Here we showed that dew formation is frequent in northern Iran. In other areas, where there was suffering from water-stress (southern and central parts of Iran), dew can be a utilized as an alternative source of water. The dew yield during 2001–2014 was lower than the overall mean during the past 40 years a result of climate change in Iran.Since water shortage has been a serious challenge in Iran, long-term investigations of alternative water resources are vital. In this study, we performed long-term (1979-2018) model simulation at seven locations (costal, desert, mountain, and urban conditions) in Iran to investigate temporal and spatial variation of dew formation. The model was developed to simulate the dew formation (water and ice) based on the heat and mass balance equation with ECMWF-ERA-Interim (European Centre for Medium-Range Weather Forecasts-Re-Analysis) meteorological data as input. According to the model simulation, the maximum mean yearly cumulative dew yield (similar to 65 L/m(2)) was observed in the mountain region in the north part of Iran with a yearly mean cumulative dew yield was similar to 36 L/m(2). The dew yield showed a clear seasonal variation at all selected locations with maximum yields in winter (mean monthly cumulative 3-8 L/m(2) depending on the location). Here we showed that dew formation is frequent in northern Iran. In other areas, where there was suffering from water-stress (southern and central parts of Iran), dew can be a utilized as an alternative source of water. The dew yield during 2001-2014 was lower than the overall mean during the past 40 years a result of climate change in Iran.Peer reviewe

Spatial and Temporal Investigation of Dew Potential based on Long-Term Model Simulations in Iran

Since water shortage has been a serious challenge in Iran, long-term investigations of alternative water resources are vital. In this study, we performed long-term (1979–2018) model simulation at seven locations (costal, desert, mountain, and urban conditions) in Iran to investigate temporal and spatial variation of dew formation. The model was developed to simulate the dew formation (water and ice) based on the heat and mass balance equation with ECMWF-ERA-Interim (European Centre for Medium-Range Weather Forecasts–Re-Analysis) meteorological data as input. According to the model simulation, the maximum mean yearly cumulative dew yield (~65 L/m2) was observed in the mountain region in the north part of Iran with a yearly mean cumulative dew yield was ~36 L/m2. The dew yield showed a clear seasonal variation at all selected locations with maximum yields in winter (mean monthly cumulative 3–8 L/m2 depending on the location). Here we showed that dew formation is frequent in northern Iran. In other areas, where there was suffering from water-stress (southern and central parts of Iran), dew can be a utilized as an alternative source of water. The dew yield during 2001–2014 was lower than the overall mean during the past 40 years a result of climate change in Iran

Modeling Long-Term Temporal Variation of Dew Formation in Jordan and Its Link to Climate Change

In this study, we performed model simulations to investigate the spatial, seasonal, and annual dew yield during 40 years (1979–2018) at ten locations reflecting the variation of climate and environmental conditions in Jordan. In accordance with the climate zones in Jordan, the dew formation had distinguished characteristics features with respect to the yield, seasonal variation, and spatial variation. The highest water dew yield (an overall annual mean cumulative dew yield as high as 88 mm) was obtained for the Mountains Heights Plateau, which has a Mediterranean climate. The least dew yield (as low as 19 mm) was obtained in Badia, which has an arid climate. The dew yield had a decreasing trend in the past 40 years due to climate change impacts such as increased desertification and the potential of sand and dust storms in the region. In addition, increased anthropogenic air pollution slows down the conversion of vapor to liquid phase change, which also impacts the potential of dew formation. The dew yield showed three distinguished seasonal patterns reflecting the three climates in Jordan. The Mountains Heights Plateau (Mediterranean climate) has the highest potential for dew harvesting (especially during the summer) than Badia (semi-arid climate)

Delineation of dew formation zones in Iran using long-term model simulations and cluster analysis

Dew is a non-conventional source of water that has been gaining interest over the last two decades, especially in arid and semi-arid regions. In this study, we performed a long-term (1979-2018) energy balance model simulation to estimate dew formation potential in Iran aiming to identify dew formation zones and to investigate the impacts of long-term variation in meteorological parameters on dew formation. The annual average of dew occurrence in Iran was similar to 102 d, with the lowest number of dewy days in summer (similar to 7 d) and the highest in winter (similar to 45 d). The average daily dew yield was in the range of 0.03-0.14 Lm(-2) and the maximum was in the range of 0.29-0.52 Lm(-2). Six dew formation zones were identified based on cluster analysis of the time series of the simulated dew yield. The distribution of dew formation zones in Iran was closely aligned with topography and sources of moisture. Therefore, the coastal zones in the north and south of Iran (i.e., Caspian Sea and Oman Sea), showed the highest dew formation potential, with 53 and 34 Lm(-2) yr(-2), whereas the dry interior regions (i.e., central Iran and the Lut Desert), with the average of 12-18 Lm(-2) yr(-2), had the lowest potential for dew formation. Dew yield estimation is very sensitive to the choice of the heat transfer coefficient. The uncertainty analysis of the heat transfer coefficient using eight different parameterizations revealed that the parameterization used in this study the Richards (2004) formulation - gives estimates that are similar to the average of all methods and are neither much lower nor much higher than the majority of other parameterizations and the largest differences occur for the very low values of daily dew yield. Trend analysis results revealed a significant (p < 0:05) negative trend in the yearly dew yield in most parts of Iran during the last 4 decades (1979-2018). Such a negative trend in dew formation is likely due to an increase in air temperature and a decrease in relative humidity and cloudiness over the 40 years.Peer reviewe

An Attempt to Utilize a Regional Dew Formation Model in Kenya

Model evaluation against experimental data is an important step towards accurate model predictions and simulations. Here, we evaluated an energy-balance model to predict dew formation occurrence and estimate its amount for East-African arid-climate conditions against 13 months of experimental dew harvesting data in Maktau, Kenya. The model was capable of predicting the dew formation occurrence effectively. However, it overestimated the harvestable dew amount by about a ratio of 1.7. As such, a factor of 0.6 was applied for a long-term period (1979–2018) to investigate the spatial and temporal variation of the dew formation in Kenya. The annual average of dew occurrence in Kenya was ~130 days with dew yield > 0.1 L/m2/day. The dew formation showed a seasonal cycle with the maximum yield in winter and minimum in summer. Three major dew formation zones were identified after cluster analysis: arid and semi-arid regions; mountain regions; and coastal regions. The average daily and yearly maximum dew yield were 0.05 and 18; 0.9 and 25; and 0.15 and 40 L/m2/day; respectively. A precise prediction of dew occurrence and dew yield is very challenging due to inherent limitations in numerical models and meteorological input parameters

ELFI

Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide variety of languages. The stand-alone ELFI graph can be used with any of the available inference methods without modifications. A central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which has recently been shown to accelerate likelihood-free inference up to several orders of magnitude by surrogate-modelling the distance. ELFI also has an inbuilt support for output data storing for reuse and analysis, and supports parallelization of computation from multiple cores up to a cluster environment. ELFI is designed to be extensible and provides interfaces for widening its functionality. This makes the adding of new inference methods to ELFI straightforward andautomatically compatible with the inbuilt features.Peer reviewe