CAR: The Clean Air Routing Algorithm for Path Navigation With Minimal PM2.5 Exposure on the Move

Mahajan, Sachit; Tang, Yu; Wu, Dong Yi; Tsai, Tzu Chieh; Chen, Ling-Jyh

doi:10.1109/access.2019.2946419

Cited by 32 publications

(12 citation statements)

References 36 publications

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“…However, this study also has limitations. For instance, this paper used the IDW interpolation for data integration, which may lead to uncertainty results; other interpolation methods like the interpolation algorithm that consider both spatial and temporal characteristics, and the hybrid kriging method [ 40 ] could be considered. Moreover, the precise quantitative and mutual effects for factors need further study, such as integrating models like the spatial autocorrelation model with BRT.…”

Section: Discussionmentioning

confidence: 99%

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

Zhang

Dai

et al. 2020

IJERPH

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As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September–November) and winter (December–February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction.

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Section: Discussionmentioning

confidence: 99%

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

Zhang

Dai

et al. 2020

IJERPH

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“…In the literature, there are not a lot of related works, but some approaches use air quality data to know the exposure level in routes [ 24 , 25 ]. Ref.…”

Section: Discussionmentioning

confidence: 99%

“…Ref. [ 24 ] offers routes with the best value of PM2.5 exposure during a route. There are some studies where sensor nodes are deployed in buses [ 25 ] or cars [ 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks

Ramos

Trilles

Muñoz

et al. 2018

Sensors

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Nowadays, citizens have a huge concern about the quality of life in their cities, especially regarding the level of pollution. Air quality level is of great importance, not only to plan our activities but also to take precautionary measures for our health. All levels of governments are concerned about it and have built their indexes to measure the air quality level in their countries, regions or cities. Taking into account the existing sensor infrastructure within smart cities, it makes possible to evaluate these indices and to know anywhere the level of pollution in real-time. In this scenario, the main objective of the current work is to foster citizens’ awareness about pollution by offering pollution-free routes. To achieve this goal, a technology-agnostic methodology is presented, which allows for creating pollution-free routes across cities depending on the level of pollution in each zone. The current work includes an extensive study of existing air quality indices, and proposes and carries forward to deployment of the defined methodology in a big city, such as Madrid (Spain).

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“…It should be noted that the Kriging method is applied in this paper due to the advantage of being smooth and intuitive in spatial interpolation. The Kriging method is a linear unbiased optimal estimation method which can give the optimal linear unbiased estimation (Mahajan et al, 2019;Wu et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

COVID-19 as a Factor Influencing Air Quality? A City Study in China

Shang

Cui

et al. 2021

Aerosol Air Qual. Res.

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In late December 2019, coronavirus disease-2019 (COVID-19) emerged. To avert the spread of the pandemic, the Chinese government immediately adopted lockdown measures such as traffic restrictions and factory closure. By analyzing the spatio-temporal distribution of AQI simply in Dongying City, a city dominated by petroleum exploration, this study aims to investigate the impact of short-term lockdown in February 2020 on the air quality. The possible influence of meteorological factors and industry exhaust emissions on the air quality were also considered and assessed. Statistically significant air quality index (AQI) reduced were observed during the short-term lockdown period compared to the same period in 2019: a 24.6% reduction with absolute decrease of 25.4.The difference between the peak and trough value of hourly average AQI during the shortterm period was nearly 1/3 that of the value in winter. The COVID-19 pandemic also had slight effects on the spatial characteristics of the AQI. Quantitative analysis demonstrated that the AQI had a strong positive correlation with exhaust emissions with p<0.01, indicating that exhaust emissions contribute to air pollution. During the short-term period, the effect of exhaust emissions on the AQI in Dongying City was significantly reduced, with about 38.3% reduction. This study inferred that the decline in AQI is related to the less movement of traffic and enterprise closure. This research provides a valuable reference for controlling air pollution in Dongying City and other areas with similar conditions.

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CAR: The Clean Air Routing Algorithm for Path Navigation With Minimal PM2.5 Exposure on the Move

Cited by 32 publications

References 36 publications

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks

COVID-19 as a Factor Influencing Air Quality? A City Study in China

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