Arctic Air Mass Characteristics Based on Observations at SMEAR I in 1998-2017

The Arctic is warming faster than any other region on Earth due to climate change. The characteristics of the air masses overlying the Arctic play a key role when assessing the magnitude and implications of global warming in the region, but comprehensive studies of Arctic air mass properties covering long time series of measurements are scarce. The aim of this study is to use such a data set to quantify the key characteristics of Arctic air masses prior to transport to the human-habited Eurasian continent, and the typical conditions leading to Arctic events in Värriö. HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model was employed to calculate backward atmospheric trajectories arriving at SMEAR I (Station for Measuring Ecosystem-Atmosphere Relations) in Värriö for every hour in 1998-2017. An air mass was classified as Arctic if the backward trajectory arriving at Värriö was located north of 78 °N 72 hours before the arrival time. Data from SMEAR I, including meteorological variables and trace gas and aerosol concentrations, were then gathered in order to compare Arctic and non-Arctic air masses. Of all the hours that were analysed, 15.0 % were classified as associated with an Arctic air mass. The typically cyclonic curvature of the trajectories and the median duration of 10 hours per individual Arctic event were hypothesised to be due to Arctic air mass events being linked to passing low pressure systems. Arctic air masses were found to be colder and have lower moisture content in summer, when the difference at surface level was 5.6 °C and 1.7 g m-3 respectively, compared to non-Arctic air masses. In other seasons the differences were less pronounced, but average particle and trace gas concentrations were found to be notably lower for Arctic air masses than for non-Arctic air masses. An exception to this was ozone, which had 24.6 % higher average concentration in Arctic air masses in months between November and February, compared to non-Arctic air masses. The annual median aerosol particle concentration in Arctic air masses was found to be 308 cm-3 and only 129 cm-3 between November and March, on average. During a median year, the value of condensation sink (CS) was on average 65 % smaller in Arctic air masses than in the non-Arctic. The Kola Peninsula industry was observed to increase concentrations of SO2 and aerosol particles, particularly Aitken mode (25-90 nm) particles, of affected air masses. Overall, Arctic air masses were found to have several unique characteristics compared to other air masses arriving at SMEAR I, Värriö. As expected, Arctic air masses are colder and drier than non-Arctic air masses, but the difference is pronounced only in summer months. Other air mass characteristics, especially aerosol particle and trace gas concentration were generally found to be lower, unless the air mass was influenced by the industrial sites in the Kola Peninsula

An enhanced integrated approach to knowledgeable high-resolution environmental quality assessment

Sustaining urban environmental quality requires effective policy measures that integrate local monitoring and contextualized high-resolution modelling with actionable scenarios. Knowledgeable decision making in this field can nowadays be supported by an array of atmospheric models, but their transfer into an Integrated Urban hydrometeorological, climate and environmental Services (IUS) remains challenging. Methodological aspects that are beyond pure technicalities of the model-to-model coupling are still poorly explored. Modeling downscaling chains lack their most user-relevant link - urban-to-neighborhood scale observations and models. This study looks at a socio-environmental context of the high-resolution atmospheric modeling in the case study of the Arctic urban cluster of Apatity and Kirovsk, Russia. We demonstrate that atmospheric dynamics of the lowermost, turbulent air layers is highly localized during the most influential episodes of atmospheric pollution. Urban micro-climates create strong circulations (winds) that are sensitive to the local environmental context. As the small-scale turbulence dynamics is not spatially resolved in meteorological downscaling or statistical modeling, capturing this local context requires specialized turbulence-resolving (large-eddy simulation) models. Societal acceptance of the urban modeling could be increased in the IUS with horizontally integrated modeling driven by localized scenarios. This study presents an enhanced integrated approach, which incorporates a large-eddy simulation model PALM into meteorological downscaling chains of a climate model (EC-EARTH), a numerical weather prediction - atmospheric chemical transport model (ENVIRO-HIRLAM) and a regional-scale meteorological model (COSMO-CLM). We discuss how this approach could be further developed into an environmental component of a digital "smart city".Peer reviewe

Aerosol particle formation in the upper residual layer

According to current estimates, atmospheric new particle formation (NPF) produces a large fraction of aerosol particles and cloud condensation nuclei in the Earth's atmosphere, which have implications for health and climate. Despite recent advances, atmospheric NPF is still insufficiently understood in the lower troposphere, especially above the mixed layer (ML). This paper presents new results from colocated airborne and ground-based measurements in a boreal forest environment, showing that many NPF events (similar to 42 %) appear to start in the topmost part of the residual layer (RL). The freshly formed particles may be entrained into the growing mixed layer (ML) where they continue to grow in size, similar to the aerosol particles formed within the ML. The results suggest that in the boreal forest environment, NPF in the upper RL has an important contribution to the aerosol load in the boundary layer (BL).Peer reviewe

Added Value of Vaisala AQT530 Sensors as a Part of a Sensor Network for Comprehensive Air Quality Monitoring

Poor air quality influences the quality of life in the urban environment. The regulatory observation stations provide the backbone for the city administration to monitor urban air quality. Recently a suite of cost-effective air quality sensors has emerged to provide novel insights into the spatio-temporal variability of aerosol particles and trace gases. Particularly in low concentrations these sensors might suffer from issues related e.g., to high detection limits, concentration drifts and interdependency between the observed trace gases and environmental parameters. In this study we characterize the optical particle detector used in AQT530 (Vaisala Ltd.) air quality sensor in the laboratory. We perform a measurement campaign with a network of AQT530 sensors in Helsinki, Finland in 2020-2021 and present a long-term performance evaluation of five sensors for particulate (PM10, PM2.5) and gaseous (NO2, NO, CO, O-3) components during a half-year co-location study with reference instruments at an urban traffic site. Furthermore, short-term (3-5 weeks) co-location tests were performed for 25 sensors to provide sensor-specific correction equations for the fine-tuning of selected pollutants in the sensor network. We showcase the added value of the verified network of 25 sensor units to address the spatial variability of trace gases and aerosol mass concentrations in an urban environment. The analysis assesses road and harbor traffic monitoring, local construction dust monitoring, aerosol concentrations from fireworks, impact of sub-urban small scale wood combustion and detection of long-range transport episodes on a city scale. Our analysis illustrates that the calibrated network of Vaisala AQT530 air quality sensors provide new insights into the spatio-temporal variability of air pollution within the city. This information is beneficial to, for example, optimization of road dust and construction dust emission control as well as provides data to tackle air quality problems arising from traffic exhaust and localized wood combustion emissions in the residential areas.Peer reviewe

Diurnal evolution of negative atmospheric ions above the boreal forest : from ground level to the free troposphere

At SMEAR II research station in Hyytiala, located in the Finnish boreal forest, the process of new particle formation and the role of ions has been investigated for almost 20 years near the ground and at canopy level. However, above SMEAR II, the vertical distribution and diurnal variation of these different atmospheric ions are poorly characterized. In this study, we assess the atmospheric ion composition in the stable boundary layer, residual layer, mixing layer, and free troposphere, and the evolution of these atmospheric ions due to photochemistry and turbulent mixing through the day. To measure the vertical profile of atmospheric ions, we developed a tailored set-up for online mass spectrometric measurements, capable of being deployed in a Cessna 172 with minimal modifications. Simultaneously, instruments dedicated to aerosol properties made measurements in a second Cessna. We conducted a total of 16 measurement flights in May 2017, during the spring, which is the most active new particle formation season. A flight day typically consisted of three distinct flights through the day (dawn, morning, and afternoon) to observe the diurnal variation and at different altitudes (from 100 to 3200 m above ground), to capture the boundary layer development from the stable boundary layer, residual layer to mixing layer, and the free troposphere. Our observations showed that the ion composition is distinctly different in each layer and depends on the air mass origin and time of the day. Before sunrise, the layers are separated from each other and have their own ion chemistry. We observed that the ions present within the stable layer are of the same composition as the ions measured at the canopy level. During daytime when the mixing layer evolved and the compounds are vertically mixed, we observed that highly oxidized organic molecules are distributed to the top of the boundary layer. The ion composition in the residual layer varies with each day, showing similarities with either the stable boundary layer or the free troposphere. Finally, within the free troposphere, we detected a variety of carboxylic acids and ions that are likely containing halogens, originating from the Arctic Sea.Peer reviewe

Measurement report : Introduction to the HyICE-2018 campaign for measurements of ice-nucleating particles and instrument inter-comparison in the Hyytiala boreal forest

The formation of ice particles in Earth's atmosphere strongly influences the dynamics and optical properties of clouds and their impacts on the climate system. Ice formation in clouds is often triggered heterogeneously by ice-nucleating particles (INPs) that represent a very low number of particles in the atmosphere. To date, many sources of INPs, such as mineral and soil dust, have been investigated and identified in the low and mid latitudes. Although less is known about the sources of ice nucleation at high latitudes, efforts have been made to identify the sources of INPs in the Arctic and boreal environments. In this study, we investigate the INP emission potential from high-latitude boreal forests in the mixed-phase cloud regime. We introduce the HyICE-2018 measurement campaign conducted in the boreal forest of Hyytiala, Finland, between February and June 2018. The campaign utilized the infrastructure of the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II, with additional INP instruments, including the Portable Ice Nucleation Chamber I and II (PINC and PINCii), the SPectrometer for Ice Nuclei (SPIN), the Portable Ice Nucleation Experiment (PINE), the Ice Nucleation SpEctrometer of the Karlsruhe Institute of Technology (INSEKT) and the Microlitre Nucleation by Immersed Particle Instrument (mu L-NIPI), used to quantify the INP concentrations and sources in the boreal environment. In this contribution, we describe the measurement infrastructure and operating procedures during HyICE-2018, and we report results from specific time periods where INP instruments were run in parallel for inter-comparison purposes. Our results show that the suite of instruments deployed during HyICE-2018 reports consistent results and therefore lays the foundation for forthcoming results to be considered holistically. In addition, we compare measured INP concentrations to INP parameterizations, and we observe good agreement with the Tobo et al. (2013) parameterization developed from measurements conducted in a ponderosa pine forest ecosystem in Colorado, USA.Peer reviewe

Measurement report : Introduction to the HyICE-2018 campaign for measurements of ice-nucleating particles and instrument inter-comparison in the Hyytiala boreal forest

The formation of ice particles in Earth's atmosphere strongly influences the dynamics and optical properties of clouds and their impacts on the climate system. Ice formation in clouds is often triggered heterogeneously by ice-nucleating particles (INPs) that represent a very low number of particles in the atmosphere. To date, many sources of INPs, such as mineral and soil dust, have been investigated and identified in the low and mid latitudes. Although less is known about the sources of ice nucleation at high latitudes, efforts have been made to identify the sources of INPs in the Arctic and boreal environments. In this study, we investigate the INP emission potential from high-latitude boreal forests in the mixed-phase cloud regime. We introduce the HyICE-2018 measurement campaign conducted in the boreal forest of Hyytiala, Finland, between February and June 2018. The campaign utilized the infrastructure of the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II, with additional INP instruments, including the Portable Ice Nucleation Chamber I and II (PINC and PINCii), the SPectrometer for Ice Nuclei (SPIN), the Portable Ice Nucleation Experiment (PINE), the Ice Nucleation SpEctrometer of the Karlsruhe Institute of Technology (INSEKT) and the Microlitre Nucleation by Immersed Particle Instrument (mu L-NIPI), used to quantify the INP concentrations and sources in the boreal environment. In this contribution, we describe the measurement infrastructure and operating procedures during HyICE-2018, and we report results from specific time periods where INP instruments were run in parallel for inter-comparison purposes. Our results show that the suite of instruments deployed during HyICE-2018 reports consistent results and therefore lays the foundation for forthcoming results to be considered holistically. In addition, we compare measured INP concentrations to INP parameterizations, and we observe good agreement with the Tobo et al. (2013) parameterization developed from measurements conducted in a ponderosa pine forest ecosystem in Colorado, USA.Peer reviewe

Mapping small watercourses with deep learning : impact of training watercourse types separately

Deep learning methods for semantic segmentation have shown great potential in automating mapping of geospatial features, including small watercourses such as streams and ditches. There are a variety of small watercourse types. In many use cases users are only interested in specific types of watercourses. However, the impact on results from neural networks trained with only some types of small watercourses, compared to all types of watercourses is not well known. We trained four deep learning models to semantically segment watercourses from an elevation model. One model was trained with all small watercourses in the labels as a single class, while three models were trained each with a single type of watercourse in the label data. The results show that training the network with a single type of watercourse results in worse recall for all three watercourse types, compared to when training all of them together. This indicates that if the goal is to get as complete set of features as possible, it is better to include all watercourse types in the training data. Future studies could use multi-class output from neural network to determine how well networks could automatically classify features when training with all small watercourses in an area

Mapping Small Watercourses from DEMs with Deep Learning—Exploring the Causes of False Predictions

Vector datasets of small watercourses, such as rivulets, streams, and ditches, are important for many visualization and analysis use cases. Mapping small watercourses with traditional methods is laborious and costly. Convolutional neural networks (CNNs) are state-of-the-art computer vision methods that have been shown to be effective for extracting geospatial features, including small watercourses, from LiDAR point clouds, digital elevation models (DEMs), and aerial images. However, the cause of the false predictions by machine-learning models is often not thoroughly explored, and thus the impact of the results on the process of producing accurate datasets is not well understood. We digitized a highly accurate and complete dataset of small watercourses from a study area in Finland. We then developed a process based on a CNN that can be used to extract small watercourses from DEMs. We tested and validated the performance of the network with different input data layers, and their combinations to determine the best-performing layer. We analyzed the false predictions to gain an understanding of their nature. We also trained models where watercourses with high levels of uncertainty were removed from the training sets and compared the results to training models with all watercourses in the training set. The results show that the DEM was the best-performing layer and that combinations of layers provided worse results. Major causes of false predictions were shown to be boundary errors with an offset between the prediction and labeled data, as well as errors of omission by watercourses with high levels of uncertainty. Removing features with the highest level of uncertainty from the labeled dataset increased the overall f1-score but reduced the recall of the remaining features. Additional research is required to determine if the results remain similar to other CNN methods