Urban air quality, meteorology and traffic linkages: Evidence from a sixteen-day particulate matter pollution event in December 2015, Beijing

Hu, Dongmei; Wu, Jianping; Tian, Kun; Liao, Lyuchao; Xu, Ming; Du, Yiman

doi:10.1016/j.jes.2017.02.005

Cited by 36 publications

(11 citation statements)

References 34 publications

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“…Larger clusters of Moran's I in comparison with Getis are presented. Our results confirm previous research that concluded that December, when fall becomes winter, is a critical month in air pollution analysis [34,35]. The reopening of schools by the end of summer also causes an increase traffic and maximizes air pollution in fall, more attention should also be paid to this risky season [12].…”

Section: Local Spatial Autocorrelation Of Pollutantssupporting

confidence: 91%

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

Habibi

Alesheikh

Mohammadinia

et al. 2017

IJGI

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Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran's I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM 2.5 ) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM 2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord's PCI suggested a more polluted air quality than the Moran's I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment.

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Section: Local Spatial Autocorrelation Of Pollutantssupporting

confidence: 91%

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

Habibi

Alesheikh

Mohammadinia

et al. 2017

IJGI

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“…The residents of the road and the motorists are most affected by these emissions. In this context, several studies have been conducted, by Hu, et al [27] who showed that air quality deteriorates due to traffic.…”

Section: Harmful Effects Of Congestionmentioning

confidence: 99%

“…Social consequences -In addition to the ecological and economic consequences, congestion has a detrimental effect on society. Speed reduction can lead to a decrease in social contacts between people, especially when the tolerated travel time is exceeded because of congestion [27]. In addition to the ecological consequences, atmospheric pollution additional increase due to this phenomenon has negative impacts on health, for example the effect of some gases on the respiratory capacity.…”

Section: Harmful Effects Of Congestionmentioning

confidence: 99%

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The impact of Planned Special Events (PSEs) on urban traffic congestion

Fernando¹

2018

ICST Transactions on Scalable Information Systems

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The transport infrastructure of many cities has not been able to keep up with the pace of growth in the motorization rate or to counteract the intensification of urban traffic. Rising in traffic congestion in cities not only impacts the productivity costing billions but also responsible for more than 40% of all CO2 emissions which results in global warming. While expansion and construction of new roads may be considered in some cases, in most, better management of existing infrastructure to lower traffic congestion is the only option.The current state of the art commercial solutions can predict the recurring traffic situations, as this behavior can be easily learned from historical data. The challenge is to predict the non-recurrent congestion caused by events such as accidents, adverse weather, construction zones and Planned Special Event (PSE). Past research has shown, PSEs such as concerts or sports games, festivals and conventions has a huge impact on everyday urban transportation. Therefore, the aim of the proposed research is to investigate the impact PSEs on urban traffic congestion.The proposed research is applying spatial-temporal big data mining methods to predict the impact of PSEs on the urban concession. Specifically, the proposed research will consider the characteristics of PSEs such as location, type, duration, audience and time and day of the event in the proposed analysis approach which enables to predict of urban congestion for future PSEs.

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“…Some studies studied the effect of traffic on particle number concentration in different seasons of the year. Meteorology and traffic emissions play a significant role in urban air quality, but relationships among them are very complicated [26,27]. Dédelé et al [28] tried to estimate the inter-seasonal differences in concentration of PM 10 at different site types.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle Number Concentration in the Air in Relation to the Time of the Year and Time of the Day

2020

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The paper analyzes suspended particles number concentrations of 61 size fractions (184 nm to 17,165 nm) in the air at a traffic location. The average course of the individual fractions was analyzed at various intervals – daily, weekly, monthly and annually, in the period between 2017 and 2019. The data was then used to calculate the arithmetic mean for all the fractions (MS Excel, R) and then using a proprietary web application, heatmaps were constructed. The obtained results showed significant differences in both the annual and daily variation of number concentrations between the individual fractions differing in particle size. In the case of the annual variation, one can see a greater variability of smaller particles, which is most likely due to the source of the actual suspended particles. Meteorological and dispersion conditions are found as important factors for suspended particle concentrations. These can lead to significant differences from year to year. However, a comparison between 2018 and 2019 showed that even though the average absolute number concentrations can differ between years, the actual relative number concentrations, i.e., the ratios between the individual fractions remain very similar. In conclusion it can be said that the difference between the number concentration variation of the size fractions depends on both the actual pollution sources (especially in the long-term, i.e., the annual variation) and the actual size of the particles, which plays a role especially in the short-term (daily, weekly variation).

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Urban air quality, meteorology and traffic linkages: Evidence from a sixteen-day particulate matter pollution event in December 2015, Beijing

Cited by 36 publications

References 34 publications

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

The impact of Planned Special Events (PSEs) on urban traffic congestion

Nanoparticle Number Concentration in the Air in Relation to the Time of the Year and Time of the Day

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