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[en] Currently, air quality information in a region becomes an important thing to know. Some efforts have been conducted to show air quality in certain region. One of the efforts that has been done is information regarding air quality in several big cities in Indonesia which can be seen in the official website of the Ministry of Environment and Forestry that have several weaknesses. One of the problems to be overcome in this research is visualization of air quality data that is monitored at one point only in which that point is the placement location of air quality monitoring station. Because of that, we need an application that can display a map of air pollution distribution using the spatial interpolation method. The solution offered is the depiction of air quality by using heatmap on the map. The method used to produce heatmap with smooth result is natural cubic spline interpolation method. The production of heatmap uses API which is provided by Google Maps. The final result obtained is the map view with the coloration in the form of color gradation in accordance with the air quality value that is obtained. (paper)
[en] This report contains a description and general instructions for the installation and use of the ''Gent'' Stacked Filter Unit (SFU) PM10 sampler. The sampler operates at a flow rate of 16 litres per min. It collects particulates which have an equivalent aerodynamic diameter (EAD) of less than 10 μm in separate ''coarse'' (2-10 μm EAD) and ''fine'' (<2 μm EAD) size fractions on two sequential 47 mm diameter Nuclepore filters. The discrimination against the >10 μm EAD particles is accomplished by a PM10 pre-impaction stage upstream of the stacked filter cassette. The air is drawn through the sampler by means of a diaphragm vacuum pump, which is enclosed in a special housing together with a needle valve, vacuum gauge, flow meter, volume meter, time switch (for interrupted sampling) and hour meter. A list of manufacturers of the various components of the sampler is also given. (author). 4 figs, 1 tab
[en] A method for presenting the health impact of emissions from furniture is introduced, which could be used in the context of environmental product declarations. The health impact is described by the negative indoor air quality potential, the carcinogenic potential, the mutagenic and reprotoxic potential, the allergenic potential, and the toxicological potential. An experimental study of emissions from four pieces of furniture is performed by testing both the materials used for production of the furniture and the complete piece of furniture, in order to compare the results gained by adding emissions of material with results gained from testing the finished piece of furniture. Calculating the emission from a product based on the emission from materials used in the manufacture of the product is a new idea. The relation between calculated results and measured results from the same products differ between the four pieces of furniture tested. Large differences between measured and calculated values are seen for leather products. More knowledge is needed to understand why these differences arise. Testing materials allows us to compare different suppliers of the same material. Four different foams and three different timber materials are tested, and the results vary between materials of the same type. If the manufacturer possesses this type of knowledge of the materials from the subcontractors it could be used as a selection criterion according to production of low emission products. -- Highlights: • A method for presenting health impact of emissions is introduced. • An experimental study of emissions from four pieces of furniture is performed. • Health impact is calculated based on sum of contribution from the materials used. • Calculated health impact is compared to health impact of the manufactured product. • The results show that health impact could be useful in product development and for presentation in EPDs
[en] We apply a simple statistical bias correction technique (empirical quantile mapping) to a long-term dataset of air quality forecasts over Europe and Italy. We used the WRF-CHIMERE modelling system, which provides operational experimental chemical weather forecast at CETEMPS (http://pumpkin.aquila.infn.it/forechem/), to simulate the years 2008-2012 at low resolution over Europe (0.5° x 0.5°) and moderate resolution over Italy (0.15° x 0.15°). We compared the simulated dataset with available observation from the European Environmental Agency database (AirBase) and characterized model skill and compliance with EU legislation using the Delta tool from FAIRMODE project (http://fairmode.jrc.ec.europa.eu/). We found that the model is generally positively biased for ozone (~50%), and negatively biased for PM10 (~50%). We show that a calibration period on 3 years of data is sufficient to greatly improve model skills and make it compliant with current European regulation. The corrected simulation is thus more reliable for operational air quality forecast and for health impact assessment.
[en] One of the important sources of air pollution and greenhouse gases is mining industry together with oil refinery. Silesia Upper Coal Basin comprise of 29 mines and 2 refineries accompanied by large steel industry and energy sector facilities. The area was directly investigated with mobile platforms under the MEMO2 project resulting of spatiotemporal map of particular sources with 3D concentration of methane, carbon dioxide, PM2.5 and PM10. Gaussian modelling applied for validation of bottom up estimates indicated large discrepancies between reported and modelled values basing on measurement results.
[en] We tested the performance in the laboratory and in the field of 24 commercial platforms (AQMesh). In carrying out this evaluation, we identified a main technical challenge associated with current commercial low-cost sensors, regarding the sensor robustness and measurement repeatability. Our results show that laboratory calibration is not able to correct for real world conditions and that it is necessary to perform a field calibration for each sensor individually. Despite that, we observed that currently some sensors are already capable of reproducing the NO2 and PM10 variability. The data from the sensors was employed to generate detailed NO2 and PM10 air quality maps using a data fusion technique. This way we were able to offer localized air quality information for the city of Oslo.
[en] The impact of cities and urban surfaces on weather, climate, and air-quality, is often assessed using modeling approach. In regional scale models, an urban parameterization in land-surface interactions can be included. This is especially important when going to higher resolution, which is common trend both in operational weather prediction and regional climate modelling as well as in coupled chemistry-transport modelling. However, model description of urban canopy related meteorological effects can significantly differ especially in the underlying surface models and the urban canopy parameterizations, which results in a certain uncertainty. To assess this uncertainty is important for adaptation and mitigation measures of the urban effects, which is often applied in the big cities, especially in connection to climate change perspective, but it is important in operational prediction as well. This is one of the main task of the project OP-PPR Proof of Concept UK as well as project Urbi Pragensi, where in addition to climate effects air-quality control aspects are studied. Effects of cities on urban and rural areas are evaluated, the impact of complexity of urban parameterization on the model results improvement is discussed with regard to demands on computational resources as well.
[en] This modeling study aims to analyze air quality and to define the key drivers of the distribution and intensity of the emissions of air pollutants and greenhouse gases in urban planning scenarios. It relies on the set up of the OLYMPUS model, which calculates anthropogenic emissions from the activity and the mobility practices of individuals. It takes into account cityspecific parameters such as morphology, population density and job centres distribution, road networks, public transport and energy consumption units, as well as climate parameters that influence the consumption of energy. OLYMPUS has been implemented for different types of urban organization. The results confirm that urban density, which is strongly correlated with the emitting urban fabric, plays a key role in the simulated air quality. However, we show that density is not the only emission control parameter of the urban fabric. Accessibility, connectivity and urban mix also play a role in the variability of emissions.
[en] AireAMG is a mobile app for air quality forecasting in the Guadalajara Metropolitan Area in Mexico. This app uses real-time air quality measurements of five criteria pollutants, along with meteorological data, to forecast air pollution concentration. A novel scheme of Artificial Neural Networks (ARN) coupled to Kalman filters is used to generate air quality predictions for up to 24 hours for each of the monitoring stations locations. We used these predictions to generate ozone and particulate matter dispersion maps, by interpolating with the Inverse Distance Weighted (IDW) method. AireAMG updates these forecasts and dispersion maps every hour, and is available for Android and iOS systems.