Browsing by Subject "urban"
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(2023)Atmospheric aerosol particles play a significant role in urban air pollution, and understanding their size distribution is essential for assessing pollution sources and urban aerosol dynamics. In this study, we use a novel method developed by Kontkanen et al. (2020) to determine size-resolved particle number emissions in the particle size range of 3-800 nm at an urban background site and a street canyon site in Helsinki. Our results show overall higher particle number emissions in the street canyon compared to the urban background. On non-NPF event days, the particle number emissions of 3-6 nm particles in the urban background are highest in the noon. The emissions to the size range of 6-30 nm are highest during the morning or afternoon at both sites, indicating traffic is the main particle source in this size range. The emissions of larger particles are relatively low. Seasonal analysis suggests higher emissions during the summer in comparison to the winter which might be linked to higher product of mixing layer height (MLH) and particle number concentration in summer. Further investigations into particle emissions from different wind sectors suggest higher particle emissions from the urban sector than from the road sector in the urban background, contrary to the results for NOx concentrations. More research is needed to better understand the underlying factors. In addition, a comparison between particle number emissions estimated using FMI measurement MLH data and ERA5 model MLH data reveals that FMI data provides a more reliable representation of the MLH in the study area. Overall, the methods show limitations in accurately capturing particle dynamics in Helsinki. Future studies should address these limitations by employing more accurate NPF event classification and refining sector division methods.
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(2021)Urban greenery is vital to the people in our increasingly urbanizing societies. It is diverse in nature and provides numerous life improving qualities. Traditionally urban greenery has been assessed with a top-down view through the sensors of aerial vehicles and satellites. This does not equate on what is experienced down at the human level. An alternative viewpoint has emerged, with the introduction of a more human-scale viewpoint. To quantify this human-scale greenery, novel and disparate approaches have been developed. However, there is little knowledge on how these modelling methods and indices manage to capture the greenery we truly experience on the ground level. This thesis is an undertaking to better understand what the greenery experienced by people on the ground level, termed humanscale greenery (HSG), means. The goal was to grasp how the various modelling methods, indices and datasets can be best used to capture this phenomenon. Simultaneously, the study tries to better comprehend how different people experience greenery. To achieve this, human-scale greenery values were collected via interviews at randomly selected study sites across Helsinki. These values were then compared to modelled values at the same sites. The methods and indices tested include modern approaches developed specifically for HSG and traditional greenery assessment methods. Along the greenery values, sociodemographic variables were collected in the interviews and compared to each other in relation to HSG values. The modelled values were on average smaller than HSG values. All methods indicated very strong or strong linear relationships with human-scale greenery. NDVI and semantic segmentation Green View Index (GVI) had the strongest relationships and least deviation. Land use (LU) and color based GVI had the highest error deviations from HSG. The sociodemographic assessment showed hints that age might affect the amount of experienced greenery, but this is uncertain. With a random sampling of interviewees, 25–34-year-olds and less nature visiting people were more common at sites with low HSG. Based on the results obtained here, many different types of novel methods are suitable for modelling HSG with strong linear relationships. However, also traditional greenery assessment methods performed well. It is difficult to curtail the experience of greenery into a single approach. A solution could possibly be obtained via the combination of methods. The results also advocate the usage of machine learning methods for greenery image segmentation. These cannot be applied everywhere due to data coverage problems, but alternative methods can also be used to fill in gaps. The significance of age on the experience of greenery needs further research. Because urban greenery’s benefits are known, attention should also be given onto how different kinds of people are able to experience it. In the future we should also discuss the meaningfulness of assessing absolute greenery in comparison to the types and parts of greenery.
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