Pollution Dispersion from a Fire Using a Gaussian Plume Model

Lotrecchiano, Nicoletta; Sofia, Daniele; Giuliano, Aristide; Poletto, Massimo

doi:10.18280/ijsse.100401

Cited by 22 publications

(15 citation statements)

References 35 publications

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“…Therefore, Lotrecchiano et al use the gaussian plume diffusion models for modeling pollutant dispersion from fires. 42 Yet, their results indicated the necessity of a high-resolution, dense network of measurement stations for improved monitoring. In addition, these physical models have spatial limitations in capturing pollutant dispersion at a large scale and weak generalization capabilities.…”

Section: ■ Related Workmentioning

confidence: 99%

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Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression

Xia,

McCracken,

Liu

et al. 2024

Environ. Sci. Technol.

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In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

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Section: ■ Related Workmentioning

confidence: 99%

“…However, these prediction models have limitations in capturing pollutant spillover. Therefore, Lotrecchiano et al use the gaussian plume diffusion models for modeling pollutant dispersion from fires . Yet, their results indicated the necessity of a high-resolution, dense network of measurement stations for improved monitoring.…”

Section: Related Workmentioning

confidence: 99%

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression

Xia,

McCracken,

Liu

et al. 2024

Environ. Sci. Technol.

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“…Using the pollutants measured concentrations it is possible to evaluate human exposure [6]. The presence of outdoor pollutants is due to various sources such as vehicular traffic, industrial emissions, domestic heating, biomass burning [7] or accidental events such as explosions or fires [8]. Pollutants, such as particulate matter, tend to form also indoors and hence accumulate with the outdoor particles [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison process of air quality measurement systems applied to the context of cultural heritage

Lotrecchiano¹,

Ricciardi²,

Pironti³

et al. 2023

Proceedings of the 2022 IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage

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Air pollution is a problem that affects both human health and the state of historical artifacts. The interaction between pollutants and historical artifacts leads to a material degradation and therefore to the ruin of the artistic heritage. To define the impact of air quality on historic artifacts, it is necessary to measure these levels through an air quality measurement system. In this study the levels of particulate matter, PM10, PM2.5 and PM1, measured in the historic sanctuary of the Beata Vergine dei Miracoli in Saronno (Varese) were analysed using two measuring devices. Two air quality sensors, operating 24/24h and 7/7 days, were installed inside the sanctuary near the "Last Supper" wooden sculptural group realized during Renaissance period by Andrea da Corbetta and decorated by Alberto da Lodi. Similar concentration values and trends were observed with the two devices. Particulate matter levels were often above the recommended values for conservation and this may pose a threat to the artifacts present in the sanctuary. The two sensors return similar values in both trends and measured concentrations. From the analysis of the particulate matter levels it can be seen that they are high and sensitive to the activities, such as religious services, that take place in the sanctuary.

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“…The available air quality data are the basis for developing mathematical models for data spatialization [26] and forecasting [27]. Furthermore, dispersion models support experimental measures to define the behavior of pollutants in the atmosphere and verify their long-term effects in case of accidental occurrence [28]. Monitoring the number of dispersed pollutants and the implementation of long-term strategies to favor their reduction is necessary to safeguard the health [29][30][31] of resident populations and travelers [32].…”

Section: Introductionmentioning

confidence: 99%

Comparison Process of Blood Heavy Metals Absorption Linked to Measured Air Quality Data in Areas with High and Low Environmental Impact

et al. 2022

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Air pollution is a problem shared by the entire world population, and researchers have highlighted its adverse effects on human health in recent years. The object of this paper was the relationship between the pollutants’ concentrations measured in the air and the quantity of pollutant itself inhaled by the human body. The area chosen for the study has a high environmental impact given the significant presence on the territory of polluting activities. The Acerra area (HI) has a waste-to-energy plant and numerous industries to which polluting emissions are attributed. This area has always been the subject of study as the numbers of cancer patients are high. A survey on male patients to evaluate the heavy metals concentrations in the blood was conducted in the two areas and then linked to its values aero-dispersed. Using the air quality data measured by the monitoring networks in two zones, one with high environmental impact (HI) and one with low environmental impact (LI), the chronicle daily intake (CDI) of pollutants inhaled by a single person was calculated. The pollutants considered in this study are PM10 and four heavy metals (As, Cd, Ni, Pb) constituting the typical particulates of the areas concerned. The CDI values calculated for the two zones are significantly higher in the HI zone following the seasonal pollution trend.

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Pollution Dispersion from a Fire Using a Gaussian Plume Model

Cited by 22 publications

References 35 publications

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression

Comparison process of air quality measurement systems applied to the context of cultural heritage

Comparison Process of Blood Heavy Metals Absorption Linked to Measured Air Quality Data in Areas with High and Low Environmental Impact

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