scholarly journals Neighbourhood-scale flow regimes and pollution transport in cities

2020 ◽  
Author(s):  
Ed Bannister ◽  
Xiaoming Cai ◽  
Jian Zhong ◽  
Rob MacKenzie
Keyword(s):  
Air Pollution ◽  
Porous Media ◽  
Urban Form ◽  
Flow Regimes ◽  
Transport Processes ◽  
Pollution Monitoring ◽  
Pollution Transport ◽  
Urban Background ◽  
Neighbourhood Scale ◽  
Quantitative Definition

<p>Cities intimately intermingle people and air pollution. However, is very difficult to assess the efficacy of air pollution policy. Permanent in-situ observations are usually too sparsely spaced to monitor transport processes within a city. The post-processing and maintenance costs associated with calibrated low-cost sensors remains too high for them simply to fill the gaps in permanent networks. The behaviour of pollutants around the scale of a neighbourhood (1-2km) remains particularly difficult to interpret and model. This gap in our understanding is unfortunate because neighbourhood-scale processes disperse pollutants from peaks beside busy roads to levels treated as the ‘urban background’, and may link urban pollution models with weather forecasts.</p><p>Urban areas can be treated as patches of porous media to which the wind adjusts by changing its mean and turbulent components. Most cities around the world are made up of lots of neighbourhoods of differing form, density and land use – e.g. commercial centres interspaced with low-rise residential neighbourhoods. For cities whose urban form varies in this way, we formulated two neighbourhood-scale flow regimes, based on the size and density of the different neighbourhood patches.</p><p>We used large-eddy simulation to investigate how these two dynamical regimes emerge in patchy neighbourhoods, and their implications for pollution policy and research. We found that these flow regimes distribute pollutants in counter-intuitive ways, such as producing pollution ‘hot spots’ in less dense patches. The flow regimes also provide: (a) a quantitative definition of the ‘urban background’, which can be used for more precisely targeted pollution monitoring; and (b) a conceptual basis for neighbourhood-scale air pollution problems and transport of fluid constituents in other porous media.</p>

scholarly journals Neighbourhood-Scale Flow Regimes and Pollution Transport in Cities

2020 ◽  
Author(s):  
Edward J. Bannister ◽  
Xiaoming Cai ◽  
Jian Zhong ◽  
A. Rob MacKenzie
Keyword(s):  
Air Pollution ◽  
Porous Media ◽  
Flow Regimes ◽  
Pollution Monitoring ◽  
Frontal Area ◽  
Pollution Transport ◽  
Computationally Efficient ◽  
Area Density ◽  
Neighbourhood Scale ◽  
Quantitative Definition

AbstractCities intimately intermingle people and air pollution. It is very difficult to monitor or model neighbourhood-scale pollutant transport explicitly. One computationally efficient way is to treat neighbourhoods as patches of porous media to which the flow adjusts. Here we use conceptual arguments and large-eddy simulation to formulate two flow regimes based on the size of patches of different frontal-area density within neighbourhoods. One of these flow regimes distributes pollutants in counter-intuitive ways, such as producing pollution ‘hot spots’ in patches of lower frontal-area density. The regimes provide the first quantitative definition of the ‘urban background’, which can be used for more precisely targeted pollution monitoring. They also provide a conceptual basis for further research into neighbourhood-scale air-pollution problems, such as parametrizations in mesoscale models, and the transport of fluid constituents in other porous media.

scholarly journals Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub>, and PM<sub>2. 5</sub> for 2001–2010

2017 ◽  
Vol 10 (4) ◽  
pp. 1767-1787 ◽  
Author(s):  
Chun Lin ◽  
Mathew R. Heal ◽  
Massimo Vieno ◽  
Ian A. MacKenzie ◽  
Ben G. Armstrong ◽  
...  
Keyword(s):  
Air Pollution ◽  
Atmospheric Chemistry ◽  
Measurement Data ◽  
Transport Processes ◽  
Daily Maximum ◽  
Model Bias ◽  
Model Framework ◽  
Urban Background ◽  
Air Pollution Epidemiology ◽  
Model Measurement

Abstract. This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km  ×  5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model–measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2. 5, and PM10 (daily maximum 8 h running mean for O3). The comparison was temporally and spatially comprehensive, covering a 10-year period (2 years for PM2. 5) and all non-roadside measurement data from the UK national reference monitor network, which applies consistent operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites for NO2, O3, PM2. 5, and PM10, respectively). Two important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – together with root mean square error, were evaluated by site type, year, month, and day-of-week. Model–measurement statistics were generally better than, or comparable to, values that allow for realistic magnitudes of measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2, and PM2. 5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2. 5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at rural background sites (median normalized mean bias (NMB) values for daily O3 and NO2 of 8 and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB  =  −29 %) and PM2. 5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km  ×  5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions (e.g. closer to traffic sources) than the grid average. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. some components of wind-blown dust. There were instances of monthly and weekday/weekend variations in the extent of model–measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model–measurement differences (aside from grid versus monitor spatial representivity) was inaccuracy of model emissions – both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals – rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.

scholarly journals Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> and PM<sub>2.5</sub> for 2001–2010

2016 ◽  
Author(s):  
C. Lin ◽  
M. R. Heal ◽  
M. Vieno ◽  
I. A. MacKenzie ◽  
B. G. Armstrong ◽  
...  
Keyword(s):  
Air Pollution ◽  
Atmospheric Chemistry ◽  
Transport Processes ◽  
Urban Traffic ◽  
Model Bias ◽  
Model Framework ◽  
Urban Background ◽  
Air Pollution Epidemiology ◽  
Pm2.5 And Pm10 ◽  
Model Measurement

Abstract. This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km × 5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model-measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2.5 and PM10 (daily maximum 8-hour running mean for O3). The comparison was temporally and spatially comprehensive covering a 10-year period (2 years for PM2.5) and all measurement data from the UK national reference monitor network, which applies consistent operational and QC/QA procedures for each pollutant (60, 49, 29 and 35 sites for NO2, O3, PM2.5 and PM10, respectively). The two most important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – were evaluated by site type, year, month and day-of-week. Model-measurement correlation and bias were generally better than values found in past studies that allowed for measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2 and PM2.5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2.5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at the background sites and least bias at rural background sites (median normalised mean bias (NMB) values for daily O3 and NO2 of 8 % and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB = −29 %) and PM2.5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km × 5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions than the grid average. This effect was particularly pronounced for comparison against urban traffic monitors, which are deliberately located close to strong sources of NOx and PM. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. wind-blown dust. There were instances of monthly and weekday/weekend variations in extent of model-measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model-measurement differences (aside from grid vs monitor spatial representivity) was inaccuracy of model emissions (both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals) rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.

A COMPARATIVE ANALYSIS AND REVIEW OF VARIOUS AIR POLLUTION MONITORING SCHEMES

2016 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
HASAN MOHD. TAHSEENUL ◽  
CHOURASIA VIJAY S. ◽  
ASUTKAR SANJAY M. ◽  
◽  
◽  
...  
Keyword(s):  
Air Pollution ◽  
Comparative Analysis ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

IoT Enabled an Open Framework for Air Pollution Monitoring System

2020 ◽  
Author(s):  
B.H. Sudantha ◽  
Manchanayaka MALSK ◽  
Nilantha Premakumara ◽  
Chamani Shiranthika ◽  
C. Premachandra ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring System ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Open Framework

Robust Environmental Sensing Using UAVs

2021 ◽  
Vol 2 (4) ◽  
pp. 1-20
Author(s):  
Ahmed Boubrima ◽  
Edward W. Knightly
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
High Performance ◽  
Weather Conditions ◽  
Ambient Air ◽  
Mission Planning ◽  
Pollution Monitoring ◽  
Compound Pollution ◽  
Planning Algorithm

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

scholarly journals COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Social Services ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rapid Spread ◽  
Air Quality Improvement ◽  
Environment Agency ◽  
Pollution Emissions ◽  
Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

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