Browsing by Subject "Air quality"

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  • Viippola, Viljami; Yli-Pelkonen, Vesa; Järvi, Leena; Kulmala, Markku; Setälä, Heikki (2020)
    Trees and other vegetation have been advocated as a mitigation measure for urban air pollution mainly due to the fact that they passively filter particles from the air. However, mounting evidence suggests that vegetation may also worsen air quality by slowing the dispersion of pollutants and by producing volatile organic compounds that contribute to formation of ozone and other secondary pollutants. We monitored nanoparticle (>10 nm) counts along distance gradients away from major roads along paired transects across open and forested landscapes in Baltimore (USA), Helsinki (Finland) and Shenyang (China) − i.e. sites in three biomes with different pollution levels − using condensation particle counters. Mean particle number concentrations averaged across all sampling sites were clearly reduced (15 %) by the presence of forest cover only in Helsinki. For Baltimore and Shenyang, levels showed no significant difference between the open and forested transects at any of the sampling distances. This suggests that nanoparticle deposition on trees is often counterbalanced by other factors, including differing flow fields and aerosol processes under varying meteorological conditions. Similarly, consistent differences in high frequency data patterns between the transects were detected only in Helsinki. No correlations between nanoparticle concentrations and solar radiation or local wind speed as affecting nanoparticle abundances were found, but they were to some extent associated with canopy closure. These data add to the accumulating evidence according to which trees do not necessarily improve air quality in near-road environments.
  • Fung, Pak L.; Zaidan, Martha A.; Timonen, Hilkka; Niemi, Jarkko V.; Kousa, Anu; Kuula, Joel; Luoma, Krista; Tarkoma, Sasu; Petäjä, Tuukka; Kulmala, Markku; Hussein, Tareq (2021)
    Air quality prediction with black-box (BB) modelling is gaining widespread interest in research and industry. This type of data-driven models work generally better in terms of accuracy but are limited to capture physical, chemical and meteorological processes and therefore accountability for interpretation. In this paper, we evaluated different white-box (WB) and BB methods that estimate atmospheric black carbon (BC) concentration by a suite of observations from the same measurement site. This study involves data in the period of 1st January 2017–31st December 2018 from two measurement sites, from a street canyon site in Mäkelänkatu and from an urban background site in Kumpula, in Helsinki, Finland. At the street canyon site, WB models performed (R² = 0.81–0.87) in a similar way as the BB models did (R² = 0.86–0.87). The overall performance of the BC concentration estimation methods at the urban background site was much worse probably because of a combination of smaller dynamic variability in the BC values and longer data gaps. However, the difference in WB (R²= 0.44–0.60) and BB models (R² = 0.41–0.64) was not significant. Furthermore, the WB models are closer to physics-based models, and it is easier to spot the relative importance of the predictor variable and determine if the model output makes sense. This feature outweighs slightly higher performance of some individual BB models, and inherently the WB models are a better choice due to their transparency in the model architecture. Among all the WB models, IAP and LASSO are recommended due to its flexibility and its efficiency, respectively. Our findings also ascertain the importance of temporal properties in statistical modelling. In the future, the developed BC estimation model could serve as a virtual sensor and complement the current air quality monitoring.
  • Prank, Marje (Finnish Meteorological Institute, 17-0)
    Finnish Meteorological Institute Contributions 128
    Atmospheric composition has strong influence on human health, ecosystems and also Earth's climate. Among the atmospheric constituents, particulate matter has been recognized as both a strong climate forcer and a significant risk factor for human health, although the health relevance of the specific aerosol characteristics, such as its chemical composition, is still debated. Clouds and aerosols also contribute the largest uncertainty to the radiative budget estimates for climate projections. Thus, reliable estimates of emissions and distributions of pollutants are necessary for assessing the future climate and air-quality related health effects. Chemistry-transport models (CTMs) are valuable tools for understanding the processes influencing the atmospheric composition. This thesis consists of a collection of developments and applications of the chemistry-transport model SILAM. SILAM's ability to reproduce the observed aerosol composition was evaluated and compared with three other commonly used CTM-s in Europe. Compared to the measurements, all models systematically underestimated dry PM10 and PM2.5 by 10-60%, depending on the model and the season of the year. For majority of the PM chemical components the relative underestimation was smaller than that, exceptions being the carbonaceous particles and mineral dust - species that suffer from relatively small amount of available oservational data. The study stressed the necessity for high-quality emissions from wild-land fires and wind-suspended dust, as well as the need for an explicit consideration of aerosol water content in model-measurement comparison. The average water content at laboratory conditions was estimated between 5 and 20% for PM2.5 and between 10 and 25% for PM10. SILAM predictions were used to assess the annual mortality attributable to short-term exposures to vegetation-fire originated PM2.5 in different regions in Europe. PM2.5 emitted from vegetation fires was found to be a relevant risk factor for public health in Europe, more than 1000 premature deaths per year were attributed to vegetation-fire released PM2.5. CTM predictions critically depend on emission data quality. An error was found in the EMEP anthropogenic emission inventory regarding the SOx and PM missions of metallurgy plants on the Kola Peninsula and SILAM was applied to estimate the accuracy of the proposed correction. Allergenic pollen is arguably the type of aerosol with most widely recognised effect to health. SILAM's ability to predict allergenic pollen was extended to include Ambrosia Artemisiifolia - an invasive weed spreading in Southern Europe, with extremely allergenic pollen capable of inducing rhinoconjuctivitis and asthma in the sensitive individuals even in very low concentrations. The model compares well with the pollen observations and predicts occasional exceedances of allergy relevant thresholds even in areas far from the plants' habitat. The variations of allergenicity in grass pollen were studied and mapped to the source areas by adjoint computations with SILAM. Due to the high year-to-year variability of the observed pollen potency between the studied years and the sparse observational network, no clear geographic pattern of pollen allergenicity was detected.
  • Su, Xiang; Liu, Xiaoli; Hossein Motlagh, Naser; Cao, Jacky; Su, Peifeng; Pellikka, Petri; Liu, Yongchun; Petäjä, Tuukka; Kulmala, Markku; Hui, Pan; Tarkoma, Sasu (2021)
    Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.
  • Zaidan, Martha Arbayani; Hossein Motlagh, Naser; Fung, Pak Lun; Lu, David; Timonen, Hilkka; Kuula, Joel; Niemi, Jarkko V; Tarkoma, Sasu; Petäjä, Tuukka; Kulmala, Markku; Hussein, Tareq (2020)
    This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.
  • Rebeiro-Hargrave, Andrew; Hossein Motlagh, Naser; Varjonen, Samu; Lagerspetz, Eemil; Nurmi, Petteri; Tarkoma, Sasu (IEEE, 2020)
    Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacy-aware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a battery-powered, portable low-cost air quality sensor design for sampling PM2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying MegaSense with citizens carrying low-maintenance portable sensors, and using smart phone exposure apps. We demonstrate daily air pollution exposure profiles and the air pollution hot-spot profile of a district. Our contributions have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.
  • Kuula, Joel (2020)
    Finnish Meteorological Institute Contributions 170
    Atmospheric particles are one of the leading mortality risk factors in the Global Burden of Disease study (GBD). The association between particulate mass of particles smaller than 2.5 μm in diameter (PM2.5) and cardiovascular and pulmonary diseases has been characterized by multiple epidemiological studies, and varying estimates suggest that several million premature death occur globally each year due to PM2.5 exposure. Mitigation of the adverse health effects of particulate matter requires comprehensive understanding of their sources and dynamic processes, such as spatial dispersion. Recent emergence and development of aerosol sensors, which are typically characterized as small, relatively low cost and easy to use, have enabled new opportunities in air quality monitoring. As a result of their practical convenience, sensors can be deployed to the field in high quantities which, consequently, enables network-type, spatially comprehensive measurements. However, with more simplified and less expensive measurement approach, less accurate and reliable results may be expected. This study aimed to evaluate and characterize the accuracy and usability of aerosol sensor to urban air quality measurements. The investigation focused on two of the most prominent measurement techniques applicable to sensor type monitoring; optical and diffusion chargingbased techniques. Sensors utilizing optical technique were evaluated in laboratory and field studies for their error sources and particle size-selectivity, specifically. Diffusion charging-based sensors, which measure lung deposited surface area of particles, were evaluated in the field for their suitability to measure combustion emitted particles, such as vehicular exhaust and residential wood combustion emissions. Results of the study indicated that optical aerosol sensors are unlikely to be fit for long-term regulatory monitoring. The main issues preventing this arise from their improper calibration which poses a significant risk of data misinterpretation; none of the laboratory evaluated sensors measured particle sizes which their technical specifications implied. On the other hand, field tests showed that when the measured size fraction was targeted to match the true detection range of the sensor, highly accurate and repeatable results were obtained. This implies that, while the usability of optical sensors is limited in their current form, the concept and vision of a sensor driven air quality monitoring network remains valid and achievable. In comparison to optical sensors, diffusion charging-based sensors were found to be more mature in terms of their technological development. The evaluated sensors exhibited accurate and stable performance throughout the test campaigns and were shown to be particularly well-suited the measurement of combustion emitted particles. Hence, diffusion charger sensors would be a valuable addition to be used alongside other measurement techniques as urban air quality is heavily affected by nanoparticles. *** Ilmakehän pienhiukkaset ovat yksi keskeisimmistä kuolleisuuden riskitekijöistä kansainvälisessä taudin rasittavuuden analyysissä. Useat epidemiologiset tutkimukset ovat osoittaneet pienhiukkasten ja sydän- ja verisuoni- sekä hengitystiesairauksien yhteyden, ja eri arvioiden mukaan useita miljoonia ennenaikaisia kuolemia tapahtuu joka vuosi pienhiukkasaltistumisen seurauksena. Jotta pienhiukkasten negatiivisiin terveysvaikutuksiin voitaisiin vaikuttaa, tulee niiden lähteet ja dynaamiset prosessit, kuten alueellinen leviäminen, tuntea hyvin. Viimeaikainen aerosolisensoreiden esilletulo ja kehittyminen ovat avanneet uusia mahdollisuuksia ilmanlaadun seurantaan. Sensorit, jotka ovat tyypillisesti pienikokoisia, suhteellisen edullisia ja helppokäyttöisiä, mahdollistavat alueellisesti kattavien sensoriverkkomittausten toteuttamisen ja siten pienhiukkasten tarkemman tutkimisen. Sensoreiden edullisempi ja siten yksinkertaisempi mittaustekniikka saattaa toisaalta johtaa suurempaan mittausepätarkkuuteen ja huonompaan laatuun. Tämän työn tavoitteena oli arvioida ja luonnehtia aerosolisensoreiden tarkkuutta ja soveltuvuutta kaupunkialueiden ilmanlaadun seurantaan. Tutkimus keskittyi kahteen mittaustekniikkaan, jotka ovat parhaiten sovellettavissa sensorityyppisiin mittauksiin; optiseen ja diffuusiovarautumiseen perustuvaan tekniikkaan. Optisia sensoreita testattiin sekä ulkoilmassa että laboratoriossa, missä niiden hiukkaskokovalikoivuutta arvioitiin tutkimalla sensorin vastetta keinotekoisesti tuotetuilla erikokoisilla referenssihiukkasilla. Diffuusiovarautumiseen perustuvia sensoreita, jotka mittaavat niin kutsuttua keuhkodeposoituvaa pinta-ala, testattiin ulkoilmassa, missä niiden suorituskykyä arvioitiin erityisesti erittäin pienten nanohiukkasten, kuten liikenteen pakokaasun sekä puunpolton päästöjen, näkökulmasta. Tutkimustulosten perusteella optiset aerosolisensorit eivät toistaiseksi ole soveltuvia pitkäaikaiseen viranomaisvalvonnassa tehtävään ilmanlaadun seurantaan. Tämä johtuu niiden virheellisestä kalibroinnista, jonka seurauksena sensorit eivät mittaa hiukkaskokoluokkia, joita niiden tekniset tuoteselosteet antavat olettaa. Riski mittausdatan väärin tulkinnalle on täten ilmeinen. Toisaalta, kun mitattu hiukkasten kokojakauma rajattiin vastaamaan sensorin ominaista vastealuetta, sensorin mittaustarkkuus oli hyvä ja toistettava. Tämän perusteella, vaikkakin virheellinen kalibrointi rajoittaa optisten sensoreiden käytettävyyttä, konsepti ja visio sensoripohjaisesta mittausverkosta on mahdollinen ja saavutettavissa. Diffuusiovarautumiseen perustuvat sensorit osoittivat olevan teknisesti kehittyneempiä kuin optiset sensorit. Testatut sensorit olivat tarkkoja ja stabiileja kaikissa kenttämittauskampanjoissa, ja ne olivat erityisen hyvin soveltuvia liikenteen pakokaasujen sekä puunpolton päästöjen mittaamiseen. Tämän vuoksi diffuusiovaraukseen perustuvat sensorit olisivat arvokas lisä muiden mittaustekniikoiden rinnalle, varsinkin kun nanohiukkasten osuus kaupunki-ilmassa on merkittävä.
  • Koivisto, Antti Joonas; Kling, Kirsten Inga; Hänninen, Otto; Jayjock, Michael; Londahl, Jakob; Wierzbicka, Aneta; Fonseca, Ana Sofia; Uhrbrand, Katrine; Boor, Brandon E.; Jiménez, Araceli Sánchez; Hämeri, Kaarle; Dal Maso, Miikka; Arnold, Susan F.; Jensen, Keld A.; Viana, Mar; Morawska, Lidia; Hussein, Tareq (2019)
    Poor air quality is a leading contributor to the global disease burden and total number of deaths worldwide. Humans spend most of their time in built environments where the majority of the inhalation exposure occurs. Indoor Air Quality (IAQ) is challenged by outdoor air pollution entering indoors through ventilation and infiltration and by indoor emission sources. The aim of this study was to understand the current knowledge level and gaps regarding effective approaches to improve IAQ Emission regulations currently focus on outdoor emissions, whereas quantitative understanding of emissions from indoor sources is generally lacking.Therefore, specific indoor sources need to be identified, characterized, and quantified according to their environmental and human health impact. The emission sources should be stored in terms of relevant metrics and statistics in an easily accessible format that is applicable for source specific exposure assessment by using mathematical mass balance modelings. This forms a foundation for comprehensive risk assessment and efficient interventions. For such a general exposure assessment model we need 1) systematic methods for indoor aerosol emission source assessment, 2) source emission documentation in terms of relevant a) aerosol metrics and b) biological metrics, 3) default model parameterization for predictive exposure modeling, 4) other needs related to aerosol characterization techniques and modeling methods. Such a general exposure assessment model can be applicable for private, public, and occupational indoor exposure assessment, making it a valuable tool for public health professionals, product safety designers, industrial hygienists, building scientists, and environmental consultants working in the field of IAQ and health. (C) 2019 The Authors. Published by Elsevier B.V.
  • Zhou, Derong; Li, Bing; Huang, Xin; Virkkula, Aki; Wu, Haisuo; Zhao, Qiuyue; Zhang, Jie; Liu, Qiang; Li, Li; Li, Chunyan; Chen, Feng; Yuan, Siyu; Qiao, Yuezhen; Shen, Guofeng; Ding, Aijun (2017)
    Highly time-resolved measurements of water soluble ions, organic and elemental carbon concentrations in the particle diameter size range D-p <2.5 mu m (PM2.5) were performed at a downwind urban site in Nanjing in the western part of the Yangtze River Delta (YRD) in eastern China during the 2014 Youth Olympic Games (YOG). In this study, we discuss the impacts of emission control in Nanjing and the surrounding areas during the YOG and regional/long-range transport on PM2.5 pollution in Nanjing. The average concentrations of NO3-, SO42-, NH4+ were 12.1 +/- 9.9, 16.5 +/- 9.2, 9.0 +/- 5.4 mu g m(-3) during the YOG, and increased 34.3%, 53.7%, 43.9% after the YOG, respectively. The control of construction or on-road soil dust and control of industry led to the decrease of Ca2+ concentration by 55% and SO2 concentration by 46%. However, SO42- concentrations remained at relatively high levels, suggesting a significant impact of regional pollution to secondary fine particles in Nanjing. Strong correlations between OC and EC were observed during and after the YOG. A higher percentage (41%) of secondary organic carbon in Nanjing during the YOG periods was consistent with high potential photochemistry and low contributions from coal combustion. Lagrangian dispersion modelling results proved that the city clusters along the Nanjing and Shanghai axis were the major source region for high PM2.5 pollution in upwind Nanjing. This work shows that short-term strict control measures could improve the air quality, especially that affected by the primary pollutants; however, regional collaborative control strategy across administrative borders in the YRD is needed for a substantial improvement of air quality.