scholarly journals Improving Correlations between Land Use and Air Pollutant Concentrations Using Wavelet Analysis: Insights from a Low-cost Sensor Network

2020 ◽  
Vol 20 (2) ◽  
pp. 314-328
Author(s):  
Naomi Zimmerman ◽  
Hugh Z. Li ◽  
Aja Ellis ◽  
Aliaksei Hauryliuk ◽  
Ellis S. Robinson ◽  
...  
Keyword(s):  
Land Use ◽  
Wavelet Analysis ◽  
Sensor Network ◽  
Low Cost ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Land use regression modelling of ambient air pollutants in Hong Kong

2020 ◽  
Author(s):  
Zhiyuan Li ◽  
Steve Hung Lam Yim ◽  
Kin-Fai Ho
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Air Quality ◽  
Air Pollutants ◽  
Ambient Air ◽  
Air Pollutant ◽  
High Rise ◽  
Pollutant Concentrations ◽  
Air Quality Measurements ◽  
Air Pollutant Concentrations

<p>Land use regression (LUR) models estimate air pollutant concentrations for areas without air quality measurements, which provides valuable information for exposure assessment and epidemiological studies. In the present study, we developed LUR models for ambient air pollutants in Hong Kong, China, a typical high-density and high-rise city. Air quality measurements at sixteen air quality monitoring stations, operated by the Hong Kong Environmental Protection Department, were collected. Moreover, five categories of predictor variables, including population distribution, traffic emissions, land use variables, urban/building morphology, and meteorological parameters, were employed to establish the LUR models of various air pollutants. Then the spatial distribution of air pollutant concentrations at 1 km × 1 km grid cells were plotted. Taking fine particle (PM2.5) as an example, the developed LUR model explained 89% of variability of PM2.5 concentrations, with a leave-one-out-cross-validation R2 of 0.64. LUR modelling results for other air pollutants will be presented. In addition, further improvements on the development of LUR models will be discussed. This study can help to assess long-term exposures to air pollutants for high-density and high-rise urban areas like Hong Kong.</p>

scholarly journals Spatial-Temporal Analysis of Air Pollutant Concentrations in Relation to Land Use and School Environment

2021 ◽  
Vol 36 (1) ◽  
pp. 568-582
Author(s):  
Healice Julit ◽  
Nafisah Khalid ◽  
Abdul Rauf Abdul Rasam ◽  
Mohamad Hezri Razali ◽  
Maisarah Abdul Halim
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
School Environment ◽  
Strong Relationship ◽  
Temporal Analysis ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Motor Vehicles ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Excessive exposure schoolchildren to air pollution can lead to long-lasting health problems, allergies and respiratory disease. It is well known that the major factors contributing to increase of air pollution are motor vehicles and industries. Thus, it is important to analyze the spatial temporal air pollutant concentrations and its relation with school location as the location of schools and its surrounding can increase their exposure. In this study, six schools in Johor were selected and the land use surrounding the schools were updated using ArcGIS. The Inverse Distance Weighting (IDW) interpolation technique was used to identify which schools’ area in Johor has a higher range of air pollutant concentration. There are four air pollution parameters obtained from the Department of Environment (DOE) which are PM2.5, CO, O3 and SO2. Hourly air pollutant concentration reading was obtained from the DOE in order to analyze air pollutant concentration during school period. The results obtained from the IDW technique showed that Sekolah Menengah Pasir Gudang (2) located in Pasir Gudang, Malaysia has reached a very unhealthy and hazardous level as compared to other schools in Johor. On the other hand, Sekolah Menengah Kebangsaan Tanjung Pengelih, Pengerang, Malaysia showed good to unhealthy range as compared to other schools in Johor. The spatial autocorrelation tool was used to analyze the relationship between the air pollution concentration and the school’s location in Johor. The results showed that the Moran’s Indices is positive showing a strong relationship that is clustering. It can be stated that there is a relationship between air pollutant concentrations with the school locations.

scholarly journals Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

2020 ◽  
Vol 17 (6) ◽  
pp. 2025
Author(s):  
Laura Goulier ◽  
Bastian Paas ◽  
Laura Ehrnsperger ◽  
Otto Klemm
Keyword(s):  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Air ◽  
Pollutant Concentrations ◽  
Day Of The Week ◽  
Artificial Neural ◽  
Input Variables ◽  
Air Pollutant Concentrations ◽  
Artificial Neural Network Ann ◽  
Performance Results

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

scholarly journals Analysis of Temporal Variations of Air Pollutant Concentrations in Ogoni Area, Niger Delta Region, Nigeria

2021 ◽  
pp. 63-73
Author(s):  
B. Yorkor ◽  
T. G. Leton ◽  
J. N. Ugbebor
Keyword(s):  
Air Pollution ◽  
Temporal Variability ◽  
Niger Delta ◽  
Air Pollutants ◽  
Temporal Variations ◽  
Time Of Day ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Hourly Data ◽  
Air Pollutant Concentrations

This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at 95 % confidence intervals. Sulphur dioxide (SO2), Nitrogen dioxide (NO2), ground level Ozone (O3) and fine particulate matter (PM2.5) concentrations exceeded permissible standards. Air pollutant concentrations showed increase in January, February, November and December compared to other months. Simulation showed that air pollutants varied significantly by hours-of-the-day and days-of-the-week and months-of-the-year. Analysis of temporal variability revealed that air pollutant concentrations increased during weekdays and decreased during weekends. The temporal variability of air pollutants in Ogoni area showed that anthropogenic activities were the main sources of air pollution in the area, therefore further studies are required to determine air pollutant dispersion pattern and evaluation the potential sources of air pollution in the area.

scholarly journals La surveillance de la qualité de l'air dans les Hauts-de-France

2021 ◽  
pp. 045
Author(s):  
Jimmy Leyes ◽  
Laure Roussel
Keyword(s):  
Air Quality ◽  
Air Pollutant ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Non Profit ◽  
The Public ◽  
Pollutant Concentrations ◽  
Ambient Concentrations ◽  
Air Pollutant Concentrations ◽  
Qualité De L’Air

La surveillance réglementaire de la qualité de l'air en France est confiée aux associations régionales agréées de surveillance de la qualité de l'air (Aasqa) telles qu'Atmo Hauts-de-France. Elles s'appuient sur une palette d'outils et leur expertise pour mesurer les polluants dans l'air de leur territoire, alerter les populations en cas d'épisode de pollution, répondre aux exigences réglementaires de surveillance définies au niveau européen, tout en prenant en compte les spécificités régionales. Cet article présente les différents outils utilisés par les Aasqa, et plus particulièrement Atmo Hauts-de-France, pour surveiller et estimer la qualité de l'air. L'association régionale opère ainsi un ensemble de stations de mesures fixes et mobiles pour suivre en continu les concentrations de polluants réglementés ou non sur son territoire, et dispose d'outils de modélisation pour évaluer et prévoir la qualité de l'air en tous points de la région. Cet article présente également certains des paramètres météorologiques qui influencent la qualité de l'air de la région Hauts-de-France, particulièrement concernée par les épisodes de pollution aux particules. Regulatory air quality monitoring in France is performed by government-approved non-profit organisations called AASQAs, one of which is Atmo Hauts-de-France. These organisations rely on decades of accumulated air quality expertise and use several techniques to measure air pollutant concentrations, inform the public when pollutant levels are unhealthy, and comply with E.U. air quality monitoring regulations. This paper gives an overview of the tools used by AASQAs, and more particularly by Atmo Hauts-de-France, to monitor and forecast air quality. The year-round continuous monitoring of air pollutant levels at fixed sites is supplemented by short-term measurements made with fully-equipped vehicles or trailers and by modelling tools that forecast air quality and estimate pollutant levels where there are no measurements. AASQAs study pollutants which ambient concentrations are regulated by European air quality standards as well as other pollutants which are not regulated in this way. This work also discusses some of the meteorological factors, that affect air quality in the region Hauts-de-France, which is heavily impacted by particulate matter pollution.

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