Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.9
Journals
Atmosphere
Special Issues
Atmospheric Trace Gas Source Detection and Quantification
share announcement

Atmospheric Trace Gas Source Detection and Quantification

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (25 February 2021) | Viewed by 17108

Special Issue Editor

Dr. Sean Coburn

E-Mail Website
Guest Editor
Precision Laser Diagnostics Laboratory, University of Colorado Boulder, Boulder, CO 80309, USA
Interests: spectroscopic measurements; instrument development; trace gas emission characterization; field-based measurement applications

Special Issue Information

Dear Colleagues,

Atmospheric trace gas measurements are a widespread research tool through which we seek to enhance our understanding of the physical and chemical processes occurring around us. Increasingly, focus in this area of research is turning towards detection and quantification of specific trace gas sources in order to inform and/or address questions with broad impacts in fields such as health and human safety, climate change, indoor and outdoor air quality, and national security/defense. We are pleased to announce that this Special Issue of Atmosphere will focus on the broad topic of atmospheric trace gas source detection and quantification. We invite researchers to submit original research manuscripts in areas related to this topic which may include, but are not limited to, techniques/methods for characterizing sources of specific trace gases, methods/frameworks for trace gas source characterization which are independent from sensing platforms, novel demonstrations of trace gas source characterization, and new proposed methods which enable trace gas source detection and quantification. Manuscripts addressing topics which would be of interest to a broad audience are preferred, but more focused studies are not necessarily discouraged.

Dr. Sean Coburn
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Trace gas measurements
  • Quantification of trace gas emissions
  • Trace gas source characterization
  • Trace gas monitoring methods
  • Technological advancements in trace gas detection

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 2565 KiB  
Article
Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age
by Kristen Okorn and Michael Hannigan
Atmosphere 2021, 12(5), 645; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12050645 - 19 May 2021
Cited by 8 | Viewed by 2885
Abstract
As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and [...] Read more.
As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility. Full article
(This article belongs to the Special Issue Atmospheric Trace Gas Source Detection and Quantification)
►▼ Show Figures

Graphical abstract

26 pages, 25251 KiB  
Article
Graz Lagrangian Model (GRAL) for Pollutants Tracking and Estimating Sources Partial Contributions to Atmospheric Pollution in Highly Urbanized Areas
by Aleksey A. Romanov, Boris A. Gusev, Egor V. Leonenko, Anastasia N. Tamarovskaya, Alexander S. Vasiliev, Nikolai E. Zaytcev and Ilia K. Philippov
Atmosphere 2020, 11(12), 1375; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11121375 - 19 Dec 2020
Cited by 15 | Viewed by 8171
Abstract
Computational modeling allows studying the air quality problems in depth and provides the best solution reducing the population risks. This research demonstrates the Graz Lagrangian model effectiveness for assessing emission sources contributions to the air pollution: particles tracking and accumulation estimate. The article [...] Read more.
Computational modeling allows studying the air quality problems in depth and provides the best solution reducing the population risks. This research demonstrates the Graz Lagrangian model effectiveness for assessing emission sources contributions to the air pollution: particles tracking and accumulation estimate. The article describes model setting up parameters and datasets preparation for the analysis. The experiment simulated the dispersion from the main groups of emission sources for real weather conditions during 96 h of December 2018, when significant excess of NO2, CO, SO2, PM10, and benzo(a)pyrene concentrations were observed in the Krasnoyarsk surface atmospheric layer. The computational domain was a parallelepiped of 40 × 30 × 2.5 km, which was located deep inside the Eurasian continent on a heterogeneous landscape exaggerated by high-rise buildings, with various pollutions sources and the ice-free Yenisei River. The results demonstrated an excellent applicability of the Lagrange model for hourly tracking of particle trajectories, taking into account the urban landscape. For values <1 MPC (maximum permissible concentration) of peak pollutants concentrations, the coincidences were 93 cases, and for values < 0.1 shares of MPC, there were 36 cases out of the total number of 97. The same was found for the average daily concentration for values <1 MPC—31, and for values <0.1 MPC—5 matches out of 44. Wind speeds COR—65.3%, wind directions COR—68.6%. The Graz Lagrangian model showed the ability to simulate air quality problems in the Krasnoyarsk greater area conditions. Full article
(This article belongs to the Special Issue Atmospheric Trace Gas Source Detection and Quantification)
►▼ Show Figures

Figure 1

20 pages, 4867 KiB  
Article
Measuring N2O Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model
by Cheng-Hsien Lin, Richard H. Grant and Cliff T. Johnston
Atmosphere 2020, 11(12), 1277; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11121277 - 26 Nov 2020
Viewed by 2116
Abstract
Nitrous oxide (N2O) emissions from agricultural soil are substantially influenced by nitrogen (N) and field management practices. While routinely soil chambers have been used to measure emissions from small plots, measuring field-scale emissions with micrometeorological methods has been limited. This study [...] Read more.
Nitrous oxide (N2O) emissions from agricultural soil are substantially influenced by nitrogen (N) and field management practices. While routinely soil chambers have been used to measure emissions from small plots, measuring field-scale emissions with micrometeorological methods has been limited. This study implemented a backward Lagrangian stochastic (bLS) technique to simultaneously and near-continuously measure N2O emissions from four adjacent fields of approximately 1 ha each. A scanning open-path Fourier-transform infrared spectrometer (OP-FTIR), edge-of-field gas sampling and measurement, locally measured turbulence, and bLS emissions modeling were integrated to measure N2O emissions from four adjacent fields of maize production using different management in 2015. The maize N management treatments consisted of 220 kg NH3-N ha−1 applied either as one application in the fall after harvest or spring before planting or split between fall after harvest and spring before planting. The field preparation treatments evaluated were no-till (NT) and chisel plow (ChP). This study showed that the OP-FTIR plus bLS method had a minimum detection limit (MDL) of ±1.2 µg m−2 s−1 (3σ) for multi-source flux measurements. The average N2O emission of the four treatments ranged from 0.1 to 2.3 µg m−2 s−1 over the study period of 01 May to 11 June after the spring fertilizer application. The management of the full-N rate applied in the fall led to higher N2O emissions than the split-N rates applied in the fall and spring. Based on the same N application, the ChP practice tended to increase N2O emissions compared with NT. Advection of N2O from adjacent fields influenced the estimated emissions; uncertainty (1σ) in emissions was 0.5 ± 0.3 µg m−2 s−1 if the field of interest received a clean measured upwind background air, but increased to 1.1 ± 0.5 µg m−2 s−1 if all upwind sources were advecting N2O over the field of interest. Moreover, higher short-period emission rates (e.g., half-hour) were observed in this study by a factor of 1.5~7 than other micrometeorological studies measuring N2O-N loss from the N-fertilized cereal cropping system. This increment was attributed to the increase in N fertilizer input and soil temperature during the measurement. We concluded that this method could make near-continuous “simultaneous” flux comparisons between treatments, but further studies are needed to address the discrepancies in the presented values with other comparable N2O flux studies. Full article
(This article belongs to the Special Issue Atmospheric Trace Gas Source Detection and Quantification)
►▼ Show Figures

Figure 1

23 pages, 6614 KiB  
Article
Source–Receptor Relationships and Cluster Analysis of CO2, CH4, and CO Concentrations in West Africa: The Case of Lamto in Côte d’Ivoire
by Dro Touré Tiemoko, Fidèle Yoroba, Jean-Daniel Paris, Adama Diawara, Antoine Berchet, Isabelle Pison, Aurélie Riandet and Michel Ramonet
Atmosphere 2020, 11(9), 903; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11090903 - 26 Aug 2020
Cited by 2 | Viewed by 2440
Abstract
The contribution in terms of long-range transport of CO2, CH4, and CO concentrations to measurements at Lamto (5°02′ W–6°13′ N) was analyzed for the 2014–2017 period using the FLEXPART model that calculates the retro-plumes of air masses arriving at [...] Read more.
The contribution in terms of long-range transport of CO2, CH4, and CO concentrations to measurements at Lamto (5°02′ W–6°13′ N) was analyzed for the 2014–2017 period using the FLEXPART model that calculates the retro-plumes of air masses arriving at the station. The identification of the source-receptor relationships was also studied with a clustering technique applied on those retro-plumes. This clustering technique enabled us to distinguish four categories of air mass transports arriving at Lamto site described as follows: oceanic and maritime origin (≈37% of the retro-plumes), continental origin (≈21%), and two hybrid clusters (≈42%). The results show that continental emission sources contribute significantly to the increases in concentrations of CO2, CH4, and CO and explain ≈40% of their variance. These emission sources are predominantly from north and north-east directions of the measurement point, and where densely populated and economically developed areas are located. In addition, the transport of air masses from these directions lead to the accumulation of CO2, CH4, and CO. Furthermore, the ratios ΔCO/ΔCH4 and ΔCO/ΔCO2 observed in the groups associated with Harmattan flows clearly show an influence of combustion processes on the continent. Thus, the grouping based on FLEXPART footprints shows an advantage compared to the use of simple trajectories for analyzing source–receptor relationships. Full article
(This article belongs to the Special Issue Atmospheric Trace Gas Source Detection and Quantification)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop