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- ItemAerosol Particle and Black Carbon Emission Factors of Vehicular Fleet in Manila, Philippines(Basel, Switzerland : MDPI AG, 2019) Madueño, Leizel; Kecorius, Simonas; Birmili, Wolfram; Müller, Thomas; Simpas, James; Vallar, Edgar; Galvez, Maria Cecilia; Cayetano, Mylene; Wiedensohler, AlfredPoor air quality has been identified as one of the main risks to human health, especially in developing regions, where the information on physical chemical properties of air pollutants is lacking. To bridge this gap, we conducted an intensive measurement campaign in Manila, Philippines to determine the emission factors (EFs) of particle number (PN) and equivalent black carbon (BC). The focus was on public utility jeepneys (PUJ), equipped with old technology diesel engines, widely used for public transportation. The EFs were determined by aerosol physical measurements, fleet information, and modeled dilution using the Operational Street Pollution Model (OSPM). The results show that average vehicle EFs of PN and BC in Manila is up to two orders of magnitude higher than European emission standards. Furthermore, a PUJ emits up to seven times more than a light-duty vehicles (LDVs) and contribute to more than 60% of BC emission in Manila. Unfortunately, traffic restrictions for heavy-duty vehicles do not apply to PUJs. The results presented in this work provide a framework to help support targeted traffic interventions to improve urban air quality not only in Manila, but also in other countries with a similar fleet composed of old-technology vehicles. © 2019 by the authors.
- ItemSupervised Machine Learning to Assess Methane Emissions of a Dairy Building with Natural Ventilation(Basel : MDPI, 2020) Hempel, Sabrina; Adolphs, Julian; Landwehr, Niels; Willink, Dilya; Janke, David; Amon, ThomasA reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.
- ItemAssessing the contribution of soil NOx emissions to European atmospheric pollution(Bristol : IOP Publ., 2021) Skiba, Ute; Medinets, Sergiy; Cardenas, Laura M.; Carnell, Edward John; Hutchings, Nick; Amon, BarbaraAtmospheric NOx concentrations are declining steadily due to successful abatement strategies predominantly targeting combustion sources. On the European continent, total NOx emissions fell by 55% between 1990 and 2017, but only modest reductions were achieved from the agricultural sector; with 7.8% from 20 Eastern European countries and 19.1% from 22 Western European countries. Consequently, the share of agricultural NOx emissions for these 42 European countries have increased from 3.6% to 7.2%. These values are highly uncertain due to serious lack of studies from agricultural soils and manure management. The emission factor (EFNO 1.33%), currently used for calculating soil NOx emissions from European agricultural categories ‘N applied to soils’ and ‘manure management’ was evaluated here by including recently published data from temperate climate zones. The newly calculated EFNO (average 0.60%, 0.0625th%/0.5475th%, n = 65 studies) is not notably different from the current value, given the large uncertainties associated with the small pool of studies, and therefore continued use of EFNO (1.33%) is recommended until more data become available. An assessment of the contribution of agricultural and non-agricultural NOx sources found that of the 42 European countries, the 8 most populated countries achieved considerable reductions (1990–2017) from categories ‘non-agricultural sources’ (55%), ‘N applied to soils’ (43%) and ‘manure management’ (1.2%), compared to small reductions from the remaining 34 countries. Forests are also large sources of soil NOx. On average, emissions from Eastern European forests were 4 times larger than from ‘N applied agricultural soil’, whereas Western European NOx emissions from ‘N applied agricultural soil’ were two times larger than from forest soils. Given that non-agricultural sources of NOx continue to decline, soil related emissions from agriculture, forests and manure management become more important, and require rigorous investigation in order to improve atmospheric pollution forecasts.