The Hourly Simulation of PM2.5 Particle Concentrations Using the Multiple Linear Regression (MLR) Model for Sea Breeze in Split, Croatia

Lesar, Tanja Trošić; Filipčić, Anita

doi:10.1007/s11270-021-05209-w

Cited by 12 publications

(1 citation statement)

References 27 publications

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“…The concentration of atmospheric pollutants varies across different regions and at different time points. , On one hand, pollutants can spill over between different areas, , and on the other hand, they can be absorbed by trees and other vegetation within a region. , Therefore, understanding the disparities of pollutant concentrations becomes highly challenging. Traditional methods for predicting pollutants primarily rely on multivariate linear regression (MLR) , approaching the data from a macro perspective and forecasting the overall concentration of pollutants for an entire region. However, this method has two main shortcomings: first, the multivariate linear regression model does not account for the nonlinear characteristics of pollutant concentration data; second, it does not consider the spatial heterogeneity and spillover effects of different regions.…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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$$\hbox {PM}_{2.5}$$ concentration prediction in Lanzhou, China, using hyperchaotic cuckoo search—extreme learning machine

Yang

Jia

et al. 2022

Stoch Environ Res Risk Assess

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LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

Shi

et al. 2023

Appl Intell

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Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

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The Hourly Simulation of PM2.5 Particle Concentrations Using the Multiple Linear Regression (MLR) Model for Sea Breeze in Split, Croatia

Cited by 12 publications

References 27 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

$$\hbox {PM}_{2.5}$$ concentration prediction in Lanzhou, China, using hyperchaotic cuckoo search—extreme learning machine

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction

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