Potential of machine learning for prediction of traffic related air pollution

Wang, An; Xu, Junshi; Tu, Ran; Saleh, Marc; Hatzopoulou, Marianne

doi:10.1016/j.trd.2020.102599

Cited by 60 publications

(23 citation statements)

References 23 publications

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“…Two machine learning models (nonlinear models) and a conventional land-use regression model (a linear model) have been adopted to predict traffic air pollution, and the normalized root-mean-square error (NRMSE) has been introduced to evaluate the models. The result showed that the values of the NRMSE for the linear model were higher, which means that the predictions were less precise [50].…”

Section: Nonlinear Relationship Of Travel and The Built Environmentmentioning

confidence: 96%

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Wang

Zhang

Zhao

et al. 2021

IJERPH

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The health and welfare of older adults have raised increasing attention due to global aging. Cycling is a physical activity and mode of transportation to enhance the mobility and quality of life among older adults. Nevertheless, the planning strategies to promote cycling among older adults are underutilized. Therefore, this paper describes the nonlinear associations of the built environment with cycling frequency among older adults. The data were collected from the Zhongshan Household Travel Survey (ZHTS) in 2012. The modeling approach was the eXtreme Gradient Boosting (XGBoost) model. The findings demonstrated that nonlinear relationships exist among all the selected built environment attributes. Within specific intervals, the population density, the land-use mixture, the distance from home to the nearest bus stop, and the distance from home to CBD are positively correlated to the cycling among older adults. Additionally, an inverse “U”-shaped relationship appears in the percentage of green space land use among all land uses. Moreover, the intersection density is inversely related to the cycling frequency among older adults. These findings provide nuanced and appropriate guidance for establishing age-friendly neighborhoods.

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Section: Nonlinear Relationship Of Travel and The Built Environmentmentioning

confidence: 96%

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Wang

Zhang

Zhao

et al. 2021

IJERPH

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“…5 Although we analyzed the feature importance for different urban form factors, the marginal effects and the monotonicity of these factors on the prediction results remain unclear. Therefore, it will be helpful to add partial dependence plots 69 and SHAP value 70 distribution of urban form factors to further explore them in future studies.…”

Section: Discussionmentioning

confidence: 99%

“…Future pilots may deploy mobile sensors within multiple cities combined with satellite-derived AOD (Aerosol Optical Depth) to further explore the spatiotemporal heterogeneity of urban air pollutants. Although we analyzed the feature importance for different urban form factors, the marginal effects and the monotonicity of these factors on the prediction results remain unclear. Therefore, it will be helpful to add partial dependence plots and SHAP value distribution of urban form factors to further explore them in future studies. …”

Section: Discussionmentioning

confidence: 99%

Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring

Tian

deSouza

Mora

et al. 2022

Environ. Sci. Technol.

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Predictive models based on mobile measurements have been increasingly used to understand the spatiotemporal variations of intraurban air quality. However, the effects of meteorological factors, which significantly affect the dispersion of air pollution, on the urban-form–air-quality relationship have not been understood on a granular level. We attempt to fill this gap by developing predictive models of particulate matter (PM) in the Bronx (New York City) using meteorological and urban form parameters. The granular PM data was collected by mobile low-cost sensors as the ground truth. To evaluate the effects of meteorological factors, we compared the performance of models using the urban form within fixed and wind-sensitive buffers, respectively. We find better predictive power in the wind-sensitive group (R = 0.85) for NC10 (number concentration for particles with diameters of 1 μm-10 μm) than the control group (R = 0.01), and modest improvements for PM2.5 (R = 0.84 for the wind sensitive group, R = 0.77 for the control group), indicating that incorporating meteorological factors improved the predictive power of our models. We also found that urban form factors account for 62.95% of feature importance for NC10 and 14.90% for PM2.5 (9.99% and 4.91% for 3-D and 2-D urban form factors, respectively) in our Random Forest models. It suggests the importance of incorporating urban form factors, especially for the uncommonly used 3-D characteristics, in estimating intraurban PM. Our method can be applied in other cities to better capture the influence of urban context on PM levels.

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“…As computer vision techniques and data-driven methods have evolved rapidly in recent years, several studies have employed convolutional neural networks (CNNs) to investigate spatial variations in UFPs using unconventional data sources, such as high-resolution satellite images − and street-level images (e.g., Google Street View panorama images), − which can provide useful information when detailed land-use data are unavailable. These studies have shown that using features extracted from images in prediction models could effectively alleviate the challenge of estimating ambient air pollution in data-sparse environments and could be extended to global cities. , These studies employed images that are generally updated infrequently, which could hinder the discovery of the associations between short-term high air pollution exposures and the local urban context.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements

Zhang

Ganji

et al. 2022

Environ. Sci. Technol.

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Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by various factors. We developed prediction models for short-term UFP exposures using street-level images collected by a camera installed on a vehicle rooftop, paired with air quality measurements conducted during a large-scale mobile monitoring campaign in Toronto, Canada. Convolutional neural network models were trained to extract traffic and built environment features from images. These features, along with regional air quality and meteorology data were used to predict short-term UFP concentration as a continuous and categorical variable. A gradient boost model for UFP as a continuous variable achieved R 2 = 0.66 and RMSE = 9391.8#/cm3 (mean values for 10-fold cross-validation). The model predicting categorical UFP achieved accuracies for “Low” and “High” UFP of 77 and 70%, respectively. The presence of trucks and other traffic parameters were associated with higher UFPs, and the spatial distribution of elevated short-term UFP followed the distribution of single-unit trucks. This study demonstrates that pictures captured on urban streets, associated with regional air quality and meteorology, can adequately predict short-term UFP exposure. Capturing the spatial distribution of high-frequency short-term UFP spikes in urban areas provides crucial information for the management of near-road air pollution hot spots.

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Potential of machine learning for prediction of traffic related air pollution

Cited by 60 publications

References 23 publications

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China

Evaluating the Meteorological Effects on the Urban Form–Air Quality Relationship Using Mobile Monitoring

Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements

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