Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

Shi, Yuan; Ng, Edward

doi:10.3390/ijerph14091008

Cited by 10 publications

(4 citation statements)

References 57 publications

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“…Sampling routes: The present study adopted a PM 2.5 sampling method which was tested in a previous pilot study on small-scale spatial variability of pedestrian level particulate matter in three downtown commercial districts [ 37 ]. To investigate the spatial variability of PM 2.5 exposure levels, one person kept strolling and rambling in a selected site area at a common pedestrian walking speed of 3 km/h (0.8 m/s) [ 38 ] with a backpack sampling unit (see the next paragraph—Sampling instrumentation).…”

Section: Methodsmentioning

confidence: 99%

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

Shi

Lau

et al. 2021

IJERPH

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Poor air quality has been a major urban environmental issue in large high-density cities all over the world, and particularly in Asia, where the multiscale complex of pollution dispersal creates a high-level spatial variability of exposure level. Investigating such multiscale complexity and fine-scale spatial variability is challenging. In this study, we aim to tackle the challenge by focusing on PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm,) which is one of the most concerning air pollutants. We use the widely adopted land use regression (LUR) modeling technique as the fundamental method to integrate air quality data, satellite data, meteorological data, and spatial data from multiple sources. Unlike most LUR and Aerosol Optical Depth (AOD)-PM2.5 studies, the modeling process was conducted independently at city and neighborhood scales. Correspondingly, predictor variables at the two scales were treated separately. At the city scale, the model developed in the present study obtains better prediction performance in the AOD-PM2.5 relationship when compared with previous studies (R2¯ from 0.72 to 0.80). At the neighborhood scale, point-based building morphological indices and road network centrality metrics were found to be fit-for-purpose indicators of PM2.5 spatial estimation. The resultant PM2.5 map was produced by combining the models from the two scales, which offers a geospatial estimation of small-scale intraurban variability.

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Section: Methodsmentioning

confidence: 99%

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

Shi

Lau

et al. 2021

IJERPH

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“…According to previous studies, the levels of concentration for PM 2.5 and O 3 in Macao often exceeded the levels recommended by the WHO AQG, and the Macao Meteorological and Geophysical Bureau (SMG) established six AQMS throughout the region of Macao, namely Macao Roadside, Macao High-Density Residential Area, Taipa Ambient, Taipa High-Density Residential Area, Coloane Ambient, and Ka-Ho Roadside [28]. The collection of PM data is difficult in a compact urban environment due to the limited roadside monitoring stations and complicated urban context, so a backpack outdoor environmental measuring unit can be used to monitor the PM in the most representative commercial districts [29]. In addition, mobile monitoring of air pollution is a growing field to fill in spatial gaps for personal air-quality-based risk assessment [30].…”

Section: Study Areamentioning

confidence: 99%

The Relationship between Roadside PM Concentration and Traffic Characterization: A Case Study in Macao

Lei

Martin

2023

Sustainability

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Road transportation is a common mode of transport in Macao and is also known to be a significant source of the emission of PM10 and PM2.5 on a local and regional scale. There are six air quality monitoring stations (AQMS) evenly distributed throughout Macao, but some densely populated areas are currently not covered by the monitoring network. Therefore, a monitoring campaign was conducted at four roadside locations in Macao’s most densely populated areas. This work aims to study the concentrations of PM10 and PM2.5 in several roadside locations in Macao. The monitoring campaign was conducted for 24 non-consecutive periods, with a total of 192 monitoring hours. The sampling sites were chosen based on Macao’s most densely populated areas and the most traffic-congested locations. In addition, traffic characterization was performed alongside the monitoring campaign to provide a clearer perspective on the pollution sources. Based on the collected data, a correlation analysis was performed between the number of vehicles and the levels of PM10 and PM2.5 concentration. The results showed a weak relationship between the hourly traffic flow and the level of PM10 and PM2.5 concentrations, with a correlation of determination (R2) of 0.001 to 0.122. In addition, the results showed a weak relationship between the vehicle types and the level of PM10 and PM2.5 concentrations, with an R2 of 0.000 to 0.043. As shown, there is little to no relationship between local traffic volume and roadside PM concentration in the monitored locations of Macao, leading us to conclude that PM concentration is more likely tied to regional sources and meteorological conditions. Nevertheless, the complex geographical setting of Macao is also likely an influential factor in this study.

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“…For example, in Poland (~312 000 km 2 ) there are only 61 governmental automatic stations for PM2.5 monitoring, which constitutes less than 35% of all stations for automatic air quality measurements. That type of monitoring system is not able to capture the fine spatial variability of PM2.5 pollution [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Sensor network for PM_2.5 measurements on an academic campus area

Badura

Sówka

Batog³

et al. 2019

E3S Web Conf.

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Fine particulate matter (PM2.5) pose a serious threat to health. Therefore it should be monitored to assess its health impacts and to take actions to reduce its pollution. However, the traditional regulatory measuring stations are not able to capture the spatial and temporal variability of PM2.5 concentrations. The opportunity to improve the resolution of PM2.5 data is based on dense networks of miniaturized low-cost sensors. The article presents the sensor network for campus area of Wrocław University of Science and Technology. This system consists of 20 sensor nodes, distributed both on a narrow scale (14 devices on the main campus area) and on a wide scale (devices on campuses in distant parts of the city). Sensor devices have been equipped with optical sensors A003 from Plantower company and with heated inlets. Dedicated website with a map is used to present the up-to-date information about air quality to the public. Messages on air quality are based on air quality index, calculated every 15 minutes. The article demonstrates also few results of preliminary measurements, when episodes of elevated PM2.5 concentrations were observed. Sensor nodes proved to be an useful tool to monitor the changes of air pollution during such events.

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Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

Cited by 10 publications

References 57 publications

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

The Relationship between Roadside PM Concentration and Traffic Characterization: A Case Study in Macao

Sensor network for PM_2.5 measurements on an academic campus area

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Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

Cited by 10 publications

References 57 publications

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

The Relationship between Roadside PM Concentration and Traffic Characterization: A Case Study in Macao

Sensor network for PM2.5 measurements on an academic campus area

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Product

Resources

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Sensor network for PM_2.5 measurements on an academic campus area