scholarly journals SIBaR: a new method for background quantification and removal from mobile air pollution measurements

2021 ◽  
Vol 14 (8) ◽  
pp. 5809-5821
Author(s):  
Blake Actkinson ◽  
Katherine Ensor ◽  
Robert J. Griffin
Keyword(s):  
Air Pollution ◽  
Markov Models ◽  
Spatial Scales ◽  
New Method ◽  
Local Source ◽  
Mobile Monitoring ◽  
Background Removal ◽  
Pollutant Concentrations ◽  
Source Contributions ◽  
Key Steps

Abstract. Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State-Informed Background Removal (SIBaR). The method employs hidden Markov models (HMMs), a popular modeling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 83 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We then assess its application to a dataset collected in Houston by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. The presented results suggest that the SIBaR-modeled source contributions contain source influences left undetected by other techniques, but that they are prone to unrealistic source contribution estimates when they extrapolate. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies while ensuring that cases of extrapolation are appropriately addressed.

scholarly journals SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements

2021 ◽  
Author(s):  
Blake Actkinson ◽  
Katherine Ensor ◽  
Robert J. Griffin
Keyword(s):  
Air Pollution ◽  
Spatial Scales ◽  
Ground Level ◽  
New Method ◽  
Local Source ◽  
Mobile Monitoring ◽  
Data Set ◽  
Detection Techniques ◽  
Background Removal ◽  
Source Contributions

Abstract. Mobile monitoring is becoming increasingly popular for characterizing air pollution on fine spatial scales. In identifying local source contributions to measured pollutant concentrations, the detection and quantification of background are key steps in many mobile monitoring studies, but the methodology to do so requires further development to improve replicability. Here we discuss a new method for quantifying and removing background in mobile monitoring studies, State Informed Background Removal (SIBaR). The method employs Hidden Markov Models (HMMs), a popular modelling technique that detects regime changes in time series. We discuss the development of SIBaR and assess its performance on an external dataset. We find 86 % agreement between the predictions made by SIBaR and the predetermined allocation of background and non-background data points. We compare five-minute averages of SIBaR-derived background NOx measurements to five-minute averages of NOx measurements taken by a stationary monitor sitting 70 m above ground level near downtown Houston, finding greater disagreement between SIBaR and the stationary monitor than the disagreement between other background detection techniques and the same stationary monitor. We then assess its application to a data set collected in Houston, TX, by mapping the fraction of points designated as background and comparing source contributions to those derived using other published background detection and removal techniques. Results suggest that SIBaR could serve as a framework for improved background quantification and removal in future mobile monitoring studies.

scholarly journals Local fractions – a method for the calculation of local source contributions to air pollution, illustrated by examples using the EMEP MSC-W model (rv4_33)

2020 ◽  
Vol 13 (3) ◽  
pp. 1623-1634 ◽  
Author(s):  
Peter Wind ◽  
Bruce Rolstad Denby ◽  
Michael Gauss
Keyword(s):  
Air Pollution ◽  
Model Simulation ◽  
Scientific Information ◽  
Urban Air Pollution ◽  
Pollution Abatement ◽  
Grid Cell ◽  
Local Source ◽  
Local Policy ◽  
Source Contributions ◽  
Air Pollution Abatement

Abstract. We present a computationally inexpensive method for individually quantifying the contributions from different sources to local air pollution. It can explicitly distinguish between regional–background and local–urban air pollution, allowing for fully consistent downscaling schemes. The method can be implemented in existing Eulerian chemical transport models and can be used to distinguish the contribution of a large number of emission sources to air pollution in every receptor grid cell within one single model simulation and thus to provide detailed maps of the origin of the pollutants. Hence, it can be used for time-critical operational services by providing scientific information as input for local policy decisions on air pollution abatement. The main limitation in its current version is that nonlinear chemical processes are not accounted for and only primary pollutants can be addressed. In this paper we provide a technical description of the method and discuss various applications for scientific and policy purposes.

scholarly journals Local Fractions – a method for the calculation of local source contributions to air pollution, illustrated by examples using the EMEP MSC-W model (rv4_33)

2019 ◽  
Author(s):  
Peter Wind ◽  
Bruce Rolstad Denby ◽  
Michael Gauss
Keyword(s):  
Air Pollution ◽  
Model Simulation ◽  
Scientific Information ◽  
Urban Air Pollution ◽  
Pollution Abatement ◽  
Grid Cell ◽  
Local Source ◽  
Local Policy ◽  
Source Contributions ◽  
Air Pollution Abatement

Abstract. We present a computationally inexpensive method for individually quantifying the contributions from different sources to local air pollution. It can explicitly distinguish between regional/background and local/urban air pollution, allowing fully consistent downscaling schemes. The method can be implemented in existing Eulerian chemical transport models and can be used to distinguish a large number of emission sources to air pollution in every receptor grid cell within one single model simulation and thus to provide detailed maps of the origin of the pollutants. Hence it can be used for time-critical operational services providing scientific information as input to local policy decisions on air pollution abatement. The main limitation in its current version is that only primary pollutants can be addressed. In this paper we provide a technical description of the method and discuss various applications for scientific and policy purposes.

scholarly journals Design of Road-Side Barriers to Mitigate Air Pollution near Roads

2021 ◽  
Vol 11 (5) ◽  
pp. 2391
Author(s):  
Jose I. Huertas ◽  
Javier E. Aguirre ◽  
Omar D. Lopez Mejia ◽  
Cristian H. Lopez
Keyword(s):  
Fluid Dynamics ◽  
Air Pollution ◽  
Computational Fluid Dynamics ◽  
Sulfur Hexafluoride ◽  
Computational Domain ◽  
Cfd Model ◽  
Near Surface ◽  
The Road ◽  
Pollutant Concentrations ◽  
Highly Correlated

The effects of using solid barriers on the dispersion of air pollutants emitted from the traffic of vehicles on roads located over flat areas were quantified, aiming to identify the geometry that maximizes the mitigation effect of air pollution near the road at the lowest barrier cost. Toward that end, a near road Computational Fluid Dynamics (NR-CFD) model that simulates the dispersion phenomena occurring in the near-surface atmosphere (<250 m high) in a small computational domain (<1 km long), via Computational Fluid Dynamics (CFD) was used. Results from the NR-CFD model were highly correlated (R2 > 0.96) with the sulfur hexafluoride (SF6) concentrations measured by the US-National Oceanic and Atmospheric Administration (US-NOAA) in 2008 downwind a line source emission, for the case of a 6m near road solid straight barrier and for the case without any barrier. Then, the effects of different geometries, sizes, and locations were considered. Results showed that, under all barrier configurations, the normalized pollutant concentrations downwind the barrier are highly correlated (R2 > 0.86) to the concentrations observed without barrier. The best cost-effective configuration was observed with a quarter-ellipse barrier geometry with a height equivalent to 15% of the road width and located at the road edge, where the pollutant concentrations were 76% lower than the ones observed without any barrier.

A Modeling study to estimate emission source contributions to the Arctic air pollution

2007 ◽  
Vol 15 ◽  
pp. 67-70
Author(s):  
Naoto MURAO ◽  
Kazuya SATOH ◽  
Sadamu YAMAGATA ◽  
Sachio OHTA
Keyword(s):  
Air Pollution ◽  
Emission Source ◽  
The Arctic ◽  
Source Contributions ◽  
Modeling Study

scholarly journals Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

2018 ◽  
Vol 52 (21) ◽  
pp. 12563-12572 ◽  
Author(s):  
Kyle P. Messier ◽  
Sarah E. Chambliss ◽  
Shahzad Gani ◽  
Ramon Alvarez ◽  
Michael Brauer ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Mobile Monitoring ◽  
Land Use Regression ◽  
Google Street View ◽  
Street View

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.

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