Nonlinear relationship between urban form and street-level PM2.5 and CO based on mobile measurements and gradient boosting decision tree models

Liu, Mengyang; Chen, Hong; Wei, Di; Wu, Yunni; Li, Chao

doi:10.1016/j.buildenv.2021.108265

Cited by 44 publications

(11 citation statements)

References 47 publications

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“…The average wind speed was far enough from 5 m/s to promote the regional transmission of particles to the city ( Chen et al., 2021 ), indicating that the impact of increasing urban permeability on the increasing wind speed and mitigation of local PM 2.5 concentrations weighed more than its effect to introduce external transmission all the time because the contribution of transboundary sources increased during the lockdown ( Sulaymon et al., 2021 ). Moreover, the findings that the decreased magnitude of BD, PR, and increased porosity mitigated the PM 2.5 concentrations agreed with some measurements and simulations conducted during a normal winter in Wuhan based on land use regression and computational simulations ( Liu et al., 2021 ; Xu & Chen, 2021 ). Large areas of water with no obstructions were also found to provide natural winds and provide good conditions for pollutant dispersal ( Crosman & Horel, 2016 ).…”

Section: Resultssupporting

confidence: 85%

Consistency of the relationship between air pollution and the urban form: Evidence from the COVID-19 natural experiment

Liu

Wei

Hong

2022

Sustainable Cities and Society

Self Cite

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Section: Resultssupporting

confidence: 85%

Consistency of the relationship between air pollution and the urban form: Evidence from the COVID-19 natural experiment

Liu

Wei

Hong

2022

Sustainable Cities and Society

Self Cite

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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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“…Gradient Boosting Machine (GBM) : GBM 28 is a ML algorithm that is used for time series prediction tasks. It works by iteratively training weak models and combining them to form a stronger predictive model.…”

Section: Air Quality Prediction Using Weighted Average Ensemble Class...mentioning

confidence: 99%

Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach

Kandan,

Jayasakthi Velmurugan,

Gururama Senthilvel

et al. 2024

Trans Emerging Tel Tech

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In recent times, air quality prediction is turned out to be one of the important research topics among research communities to prevent lives from negative health impacts. Random fluctuations of PM2.5 level brought about by frequent variations in meteorological factors create difficulties air pollution management. Forecasting the quality of air using time series data serves as a defense mechanism against threatening hazards by providing immense support to take preventive measures. Besides, handling dynamic real time workloads, forecasted by the prediction model requires appropriate computing resources to distribute workloads based on demands. To achieve this goal, this paper proposes a new Air Quality Prediction‐enabled Resource Allocation scheme for cloud‐based software services, which offers dynamic adjustment of resources based on workload demands with high energy efficiency. The proposed system is a two phase system that executes both air quality prediction and resource allocation processes consecutively. A new weighted average ensemble classifier is designed by combining support vector machine (SVM), artificial neural network (ANN), and gradient boosting machine (GBM) techniques to measure PM2.5 level on time series information of Beijing PM2.5 dataset. The system then dynamically allocates appropriate computing resources using crossover particle swarm optimization (CPSO) algorithm based on the forecasted results of PM2.5 level. This system has the potential to contribute significantly to reducing energy consumption and improving air quality in cities worldwide. The experimental results conducted to determine the efficiency of the proposed system in terms of different metrics proves that it achieves greater performance with less error functions for PM2.5 level prediction as well as minimizes energy consumption for resource allocation when compared with existing methods.

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Nonlinear relationship between urban form and street-level PM2.5 and CO based on mobile measurements and gradient boosting decision tree models

Cited by 44 publications

References 47 publications

Consistency of the relationship between air pollution and the urban form: Evidence from the COVID-19 natural experiment

Consistency of the relationship between air pollution and the urban form: Evidence from the COVID-19 natural experiment

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

Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach

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