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For the last couple of decades, environmental protection awareness within port areas is gaining ever more importance. Ports can have a tremendous impact on the environment, especially in terms of air pollution. The main pollution sources are various port activities such as road and rail traffic, cargo handling and marine vessel operations. Air quality models can be of great help in estimating the effect on the ambient air quality from one or more sources emitting pollutants to the atmosphere. One of those models is the widely used Gaussian Plume dispersion approach. Based on existing measurements and port activity data, models can simulate the dispersion of air pollutants caused by activities and operations taking place within the port. By using historical data, they can simulate the current state of the air quality in the port and with the help of weather predictions simulate possible future situation. Simulations can assist the port manager/operator in the decision-making process in order to optimize various activities within the port and minimize their impact on the environment. One of the main objectives of the Horizon 2020 Project PIXEL (Port IoT for environmental leverage) is the deployment of environmental pollution models which can aid in the decision-making processes within the port domain. This paper reviews the current advances in the field of air pollution modelling with a special emphasis on port scenarios.

Air pollution generated from airport activities has become public concern and the subject of more rigorous government regulations. The Airport Operators are stipulated to control the pollution and for the accountability of air quality that might affect public health. The main objective of this study is to establish a model for the distribution of air pollutants and to predict their concentrations generated by the runway and apron operations at Sam Ratulangi International Airport (Manado) until 2024, in accordance with the airport expansion program. The data was collected in the airport surrounding area in 2018, while the climate data over a span of 10 years, from 2009 to 2018, was obtained from Sam Ratulangi Meteorological Station. The modeling on dispersion of air pollutant gases was developed by the Gaussian Plume Equation. The simulation was performed using AERMOD software, and the results visualized by GIS software. AERMOD software was recommended by the US-EPA to predict the impact of air pollutants. The results predicted that the maximum concentrations of NOx; HC; and CO generated by runway activities modeling in 2024 were 250 μg.m-3; 6.4 μg.m-3; and 87 μg.m-3 respectively. The results also predicted that the maximum concentrations of NOx; CO; and PM10 due to apron operational activities in 2024 were 260 μg.m-3; 892 μg.m-3; and 2.5 μg.m-3 respectively. The model predicted that in 2024 the air pollution at Sam Ratulangi International Airport will remain under the limit as defined in Indonesian Government Regulation No. 22 of 2021. To mitigate the future increase in air emissions due to the increase in airport capacity, the recommendation were proposed in the several areas, which were including operation management, technology, policies and airport regulations, as well as the provision of green area.

Nach Inkrafttreten der Technischen Anleitung zur Reinhaltung der Luft (TA Luft) 2021 wird die Verwendung von Niederschlagsdaten des Umweltbundesamtes (UBA) für Ausbreitungsrechnungen bestimmter Gase obligatorisch, was bestimmte Konsequenzen nach sich zieht, die in diesem Artikel erörtert werden. Der begrenzte Zeitraum der Verfügbarkeit der Niederschlagsdaten von 2006 bis 2015 bringt Konflikte mit Regelungen der Richtlinie VDI 3783 Blatt 20 mit sich, was dazu führt, dass bei Beachtung aller Regelungen eine Reihe von Messstationen nicht mehr für eine Übertragung im Sinne dieser Richtlinie zur Verfügung steht und die ohnehin knappe Datenbasis an übertragbaren Stationen weiter verarmt. Zudem führt der begrenzte Zeitraum der Verfügbarkeit dazu, dass bei der Auswahl von repräsentativen Jahren derzeit keine Zeiträume nach 2015 mehr gefunden werden können. Die Tatsache, dass die stundenfein aufgelösten und standortspezifischen Niederschlagsdaten des UBA bei der Anwendung in der Ausbreitungsrechnung einen zeitlichen Versatz zu den von einer Station übertragenen Messwerten für Wind und Stabilität haben können, stellt eine systematische Fehlerquelle bei der Bestimmung der nassen Deposition dar. Eine Betrachtung von Positionen entlang einer Schnittlinie zwischen zwei Messstationen hat gezeigt, dass die standortspezifischen Niederschlagsdaten das unterliegende Geländeprofil plausibel abbilden, d. h., die räumliche Repräsentativität der Niederschlags- daten des UBA ist gegeben.

Summarization: Aircraft emissions from Landing and Take-Off (LTO) cycles at Chania airport (Crete), Greece were estimated for the year 2016 adopting the International Civil Aviation Organization (ICAO) methodology and using daily data from air traffic. The AERMOD Gaussian dispersion model was elaborated to determine the ground-level concentrations of air pollutants emitted from the aircraft engines. Emissions of CO, NOx as NO2, SO2, CO2, PM2.5 mass, and particle number from aircraft engines were evaluated and ground-level concentrations of these pollutants were determined. The aircraft emissions were mainly derived from the ground-level parts of the LTO cycle. The AERMOD model referring to the 1-h average concentrations has revealed that there were 20 exceedances of NO2 concentrations above the value of 200 μg/m3; two more than the regulated threshold described in the European Union Directive 2008/50/EC. The exceedances were calculated mostly during the summer period which coincides with the touristic period. High number concentrations of particles were also simulated close to the airport with yearly average values close to 10,000 particles per cm3 at the airport area. Contrary, the contribution from aircraft LTO cycles to the ground-level concentration of CO, SO2, and PM2.5 mass was below the air quality threshold values. ; Παρουσιάστηκε στο: Air Quality, Atmosphere and Health

Even though emission inventories indicate that wood combustion is a major source of polycyclic aromatic hydrocarbons (PAHs), estimating its impacts on PAH concentration in ambient air remains challenging. In this study the effect of local small-scale wood combustion on the benzo[ a ]pyrene (BaP) concentrations in ambient air in the Helsinki metropolitan area in Finland is evaluated, using ambient air measurements, emission estimates, and dispersion modeling. The measurements were conducted at 12 different locations during the period from 2007 to 2015. The spatial distributions of annual average BaP concentrations originating from wood combustion were predicted for four of those years: 2008, 2011, 2013, and 2014. According to both the measurements and the dispersion modeling, the European Union target value for the annual average BaP concentrations (1 ng m −3 ) was clearly exceeded in certain suburban detached-house areas. However, in most of the other urban areas, including the center of Helsinki, the concentrations were below the target value. The measured BaP concentrations highly correlated with the measured levoglucosan concentrations in the suburban detached-house areas. In street canyons, the measured concentrations of BaP were at the same level as those in the urban background, clearly lower than those in suburban detached-house areas. The predicted annual average concentrations matched with the measured concentrations fairly well. Both the measurements and the modeling clearly indicated that wood combustion was the main local source of ambient air BaP in the Helsinki metropolitan area.