Research Papers

Permanent URI for this collection

https://gmitlibrary.net/handle/123456789/18

Browse

Browsing Research Papers by Subject "air pollution"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City
    (2022) Bulgansaikhan Baldorj; Munkherdene Tsagaan; Lodoysamba Sereeter; Amanjol Bulkhbai
    Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R 2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.
  • No Thumbnail Available
    Item
    Pollution reduction potential by implementing electrostatic dust precipitators on mongolian small-scale stoves (a pilot study in Ulanbaatar)
    (2020) Daniel Karthe; Tim Hafer; Byambasuren Battulga; Lodoysamba Sereeter; Gunther Stehr
    The Mongolian capital of Ulaanbaatarexperiences some of the world’s worst air pollution during the winter months, most of it being caused by small coal- and wood-fired stoves which are used for heating and cooking purposes in peri-urban parts of the city. A recent pilot study in Songinokhairkhan District of Ulaanbaatar City evaluated the feasibility of electrostatic dust precipitators (ESP) for reducing particulate matter (PM) emissions from small stoves. This paper focuses on the pollution reduction potentials that would result from a large-scale implementation of ESPs. Using a locally developed low- cost ESP system (which is currently in the process of further improvement), reduction rates ranging between 10 to 50% of the PM emissions (depending on the fuel and combustion conditions) could be achieved. Fitting all or at least a major fraction of the small stoves with such ESPs could reduce PM emissions by an order of several thousand tons per heating season for the whole city. The avoided particle emissions would simultaneously prevent atmospheric pollution by various trace metals and metalloids including As, Cd, Pb and Zn, which are known to be major soil and water pollutants locally, and several other toxic substances. However, this also means that safe disposal strategies must be developed for the fly ash precipitated during ESP operation.