scholarly journals Urban Air Quality: Assessing Traffiffic and Building Architecture Impacts using Portable Measuring Devices in Toronto, Ontario

2019 ◽  
Vol 5 (1) ◽  
pp. 5-12
Author(s):  
Cuilian Fang ◽  
Cheol H. Jeong ◽  
Greg J. Evans
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Ultrafine Particles ◽  
Tall Buildings ◽  
Urban Pollution ◽  
Urban Air Quality ◽  
Urban Air ◽  
Portable Devices ◽  
Pollution Levels ◽  
King Street

Vehicle emissions are one of the largest local contributors to poor urban air quality. High emissions are often associated with traffic congestion, and pollution may also become trapped between tall buildings creating a street canyon effect. The spatial variability of traffic-related air pollutants in microenvironments should be considered in evaluating changes in urban planning. This study focuses on assessing the air quality and commuter exposure in Toronto, Ontario, Canada, specifically focusing on the effect of the King Street Pilot Project on local urban air quality by reducing traffic. Increased vehicular density is expected to contribute to higher urban pollution levels and tall buildings may trap these contaminants. Field measurements were made within the King Street Pilot area during weekday rush hours to capture the best representation of peak activity and pollutant levels when there were similar average wind speeds and directions for the sampling dates. A suite of portable devices was carried along predesigned and timed routes through traffic dense areas to measure vehicle-related air pollutants including black carbon (BC), ultrafine particles (UFP, particles smaller than 0.1 μm), and particulate matter (PM2.5, particles smaller than 2.5 μm). Data was normalized, corrected and analyzed using centralized pollutant while considering meteorological site measurements located about 1.5 km away from the study area. Results indicated higher BC and UFP levels during peak commuting times between 8 am to 10 am and relatively increased pollution levels within the area of tall buildings versus the area with shorter buildings. Strong spatial variations of BC and UFP were found, while PM2.5 levels remained relatively constant in the downtown area. Elevated levels of BC and UFP were observed around nearby construction sites. This study contributes to establishing a baseline to evaluate the King Street Pilot Project’s air quality impact as well as proposing potential methods of detailed data collection within microenvironments to observe the air quality of urban centres.

scholarly journals The importance of measuring ultrafine particles in urban air quality monitoring in small cities

2019 ◽  
Vol 23 (4) ◽  
pp. 347-358 ◽  
Author(s):  
Ivan Notardonato ◽  
Maurizio Manigrasso ◽  
Luigi Pierno ◽  
Gaetano Settimo ◽  
Carmela Protano ◽  
...  
Keyword(s):  
Air Quality ◽  
Ultrafine Particles ◽  
Urban Air Quality ◽  
Quality Monitoring ◽  
Urban Air ◽  
Air Quality Monitoring ◽  
Small Cities

scholarly journals VISUALISING URBAN AIR QUALITY USING AERMOD, CALPUFF AND CFD MODELS: A CRITICAL REVIEW

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 355-363
Author(s):  
N. Ridzuan ◽  
U. Ujang ◽  
S. Azri ◽  
T. L. Choon
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
Air Pollutants ◽  
Dispersion Model ◽  
Ambient Air ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Modelling ◽  
Modeling Tools ◽  
Wide Range

Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.

Human exposure assessment to air pollutants: application of a new portable air monitoring instrument

2020 ◽  
Author(s):  
Sheng Ye ◽  
Mark Wenig
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Urban Pollution ◽  
Air Monitoring ◽  
Raspberry Pi ◽  
Data Logging ◽  
Data Set ◽  
Individual Exposure ◽  
Pollution Levels ◽  
Human Exposure Assessment

<p>Air pollution has been gaining increasing global attention. The public is concerned about urban pollution levels including both in- and outdoor air quality. A handheld Air Quality Inspection Box (Airquix) was developed in order to monitor air pollutants in real-time, and determine individual exposure to different pollutants in different environments. The Airquix is equipped with air quality sensors: electrical chemical NO<sub>2</sub>, O<sub>3</sub>, NO sensors, NDIR CO<sub>2</sub> sensor, OPC-N3 PM sensor; environment sensor (T, RH, P), GPS sensor and a raspberry pi for data logging, processing and display. To achieve a relatively high accuracy, e.g. +/- 5ppb at 5 seconds time resolution for the NO<sub>2</sub> concentration, the pre- and post- calibration for the Airquix were performed by comparison with high-end air monitoring instruments. In this study, several Airquixes were distributed to different persons to assess individual exposure. The daily activities were distinguished by different commutes, in- and outdoor behaviors, the personal habits and potential episodes. The resulting data set can be used for the assessment of health impacts.</p>

Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

2020 ◽  
Author(s):  
Philipp Schneider ◽  
Nuria Castell ◽  
Paul Hamer ◽  
Sam-Erik Walker ◽  
Alena Bartonova
Keyword(s):  
Sensor Networks ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Low Cost ◽  
Urban Air Quality ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Model ◽  
Quality Model

<p>One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM<sub>2.5</sub> observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.</p><p>In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.</p><p>While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.</p><p> </p><p>Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.</p>

scholarly journals Urban Pollution

2021 ◽  
pp. 243-258
Author(s):  
Janet E. Nichol ◽  
Muhammad Bilal ◽  
Majid Nazeer ◽  
Man Sing Wong
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Air Quality ◽  
Urban Areas ◽  
Urban Pollution ◽  
Urban Air Quality ◽  
Quality Monitoring ◽  
Urban Air ◽  
Retrieval Algorithms ◽  
Urban Heat

AbstractThis chapter depicts the state of the art in remote sensing for urban pollution monitoring, including urban heat islands, urban air quality, and water quality around urban coastlines. Recent developments in spatial and temporal resolutions of modern sensors, and in retrieval methodologies and gap-filling routines, have increased the applicability of remote sensing for urban areas. However, capturing the spatial heterogeneity of urban areas is still challenging, given the spatial resolution limitations of aerosol retrieval algorithms for air-quality monitoring, and of modern thermal sensors for urban heat island analysis. For urban coastal applications, water-quality parameters can now be retrieved with adequate spatial and temporal detail even for localized phenomena such as algal blooms, pollution plumes, and point pollution sources. The chapter reviews the main sensors used, and developments in retrieval algorithms. For urban air quality the MODIS Dark Target (DT), Deep Blue (DB), and the merged DT/DB algorithms are evaluated. For urban heat island and urban climatic analysis using coarse- and medium- resolution thermal sensors, MODIS, Landsat, and ASTER are evaluated. For water-quality monitoring, medium spatial resolution sensors including Landsat, HJ1A/B, and Sentinel 2, are evaluated as potential replacements for expensive routine ship-borne monitoring.

scholarly journals Urban Air Quality Guidance Based on Measures Categorization in Road Transport

2021 ◽  
Vol 7 (2) ◽  
pp. 253-267
Author(s):  
Beti Angelevska ◽  
Vaska Atanasova ◽  
Igor Andreevski
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Road Transport ◽  
Urban Air Quality ◽  
Mitigation Measures ◽  
Annual Reports ◽  
Urban Air ◽  
Air Pollution Control ◽  
Pollution Levels

Air pollution is a cause for serious concerns in urban areas in Republic of North Macedonia. Intensive development of road transport increases the main air pollutants’ concentrations - particulate matter and nitrogen dioxide, whose monitored values are continuously exceeding the limit. The main disadvantage of the national plans and annual reports is the absence of comprehensive and categorized list of reduction/mitigation measures for road transport impacts on air quality. Analyzing the current air pollution problem and road transport contribution this paper provides the needed and detailed categorization of short-to-long term reduction/mitigation measures consisting of five subcategories. Based on measure categorization, a guiding frame for urban air quality is designed, intended for further support and assistance for local authorities in the process of air pollution control. Designed with integrated activities, the air quality guidance enables them to select suitable measures to manage road transport pollution and to evaluate their effects estimating the changes in air pollution levels. Hence, the guidance can be used for thorough planning of air quality issues caused by road transport and for policy making. Contributing for urban air quality improvement the guidance is a first step towards the implementation of air pollution management in urban areas. Doi: 10.28991/cej-2021-03091651 Full Text: PDF

Sign in / Sign up

Export Citation Format

Share Document