Evaluation of PM Chemical Composition in Thessaloniki, Greece Based on Air Quality Simulations

Tsiaousidis, Dimitrios Theodoros; Liora, Natalia; Kontos, Serafim; Poupkou, Anastasia; Akritidis, Dimitris; Melas, Dimitrios

doi:10.3390/su151310034

Cited by 2 publications

(2 citation statements)

References 61 publications

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“…Thus, emissions from Thessaloniki's urban, industrial, and port activities [39] may also have a negative contribution to the municipality of Thermi's air-quality conditions. In general, domestic biomass burning has been identified as one of the major air-quality issues among southeastern European countries [40] and Thessaloniki [41][42][43]. This is the case also for the greater Thermi area where prolonged biomass burning activities from the residential heating sector comprise during the winter the most abundant PM source.…”

Section: Study Areamentioning

confidence: 99%

Forecasting the Exceedances of PM2.5 in an Urban Area

Logothetis,

Kosmopoulos,

Panagopoulos

et al. 2024

Atmosphere

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Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality.

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Section: Study Areamentioning

confidence: 99%

Forecasting the Exceedances of PM2.5 in an Urban Area

Logothetis,

Kosmopoulos,

Panagopoulos

et al. 2024

Atmosphere

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“…In general, transport models allow for somewhat coarse modeling of large spatial scales, from 100m (ADMS-5) to a whole hemi-sphere (CAMx), while CFD models allow for very detailed modeling, though focused on small scales. It is shown in [15] that dispersion models (e.g., [22]) are less accurate within the complex agglomeration of a city; thus, a prominent approach would be to employ a CFD framework in such environments. However, this task could be very costly in terms of computational resources and, for some applications, practically impossible.…”

Section: Introductionmentioning

confidence: 99%

A Novel AI Framework for PM Pollution Prediction Applied to a Greek Port City

Anagnostopoulos,

Rigas,

Papachristou

et al. 2023

Atmosphere

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Particulate matter (PM) pollution is a major global concern due to its negative impact on human health. To effectively address this issue, it is crucial to have a reliable and efficient forecasting system. In this study, we propose a framework for predicting particulate matter concentrations by utilizing publicly available data from low-cost sensors and deep learning. We model the temporal variability through a novel Long Short-Term Memory Neural Network that offers a level of interpretability. The spatial dependence of particulate matter pollution in urban areas is modeled by incorporating characteristics of the urban agglomeration, namely, mean population density and mean floor area ratio. Our approach is general and scalable, as it can be applied to any type of sensor. Moreover, our framework allows for portable sensors, either mounted on vehicles or used by people. We demonstrate its effectiveness through a case study in Greece, where dense urban environments combined with low cost sensor networks is a peculiarity. Specifically, we consider Patras, a Greek port city, where the net PM pollution comes from a variety of sources, including traffic, port activity and domestic heating. Our model achieves a forecasting accuracy comparable to the resolution of the sensors and provides meaningful insights into the results.

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Evaluation of PM Chemical Composition in Thessaloniki, Greece Based on Air Quality Simulations

Cited by 2 publications

References 61 publications

Forecasting the Exceedances of PM2.5 in an Urban Area

Forecasting the Exceedances of PM2.5 in an Urban Area

A Novel AI Framework for PM Pollution Prediction Applied to a Greek Port City

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