The Ginninderra CH4 and CO2 release experiment: An evaluation of gas detection and quantification techniques

A methane (CH4) and carbon dioxide (CO2) release experiment was held from April to June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. Eight quantification techniques were assessed: three tracer ratio techniques (two mobile); backwards Lagrangian stochastic modelling; forwards Lagrangian stochastic modelling; Lagrangian stochastic (LS) footprint modelling; atmospheric tomography using point and using integrated line sensors. The majority of CH4 estimates were within 20% of the actual CH4 release rate (5.8 g/min), with the tracer ratio technique providing the closest estimate to both the CH4 and CO2 release rates (100 g/min). Once the release rate was known, the majority of revised estimates were within 10% of the actual release rate. The study illustrates the power of measuring the emission rate using multiple simultaneous methods and obtaining an ensemble median or mean. An ensemble approach to estimating the CH4 emission rate proved successful with the ensemble median estimate within 16% for the actual release rate for the blind release experiment and within 2% once the release rate was known. The release also provided an opportunity to assess the effectiveness of stationary and mobile ground and aerial CH4 detection technologies. Sensor detection limits and sampling rates were found to be significant limitations for CH4 and CO2 detection. A hyperspectral imager\u27s capacity to image the CH4 release from 100 m, and a Boreal CH4 laser sensor\u27s ability to track moving targets suggest the future possibility to map gas plumes using a single laser and mobile aerial reflector

Road Dust in Urban and Industrial Environments: Sources, Pollutants, Impacts, and Management

Road dust (RD) is one of the most important sources of particles in the atmosphere, especially in industrial areas and cities. In this special issue, we collected 16 original articles that describe field, experimental, and modeling studies related to RD and its various size fractions as a key issue in understanding the relationships between several urban and industrial environments and in the identification of pollution sources. Articles in the special issue focus primarily on the following main topics: (1) study of the chemical composition and speciation of RD and its source attribution; (2) assessment of RD and aerosol pollution levels (including express technique), environmental hazards and public health risks; (3) distribution of stable and radioactive isotopes in RD; (4) determination of factors affecting the level of dust accumulation on roads and the intensity of its pollution; and (5) study of the effect of RD on the atmosphere and other environments. Based on the results presented in this special issue, but not limited to, some of the current challenges in studying RD are formulated, including the need for further geographically wider and analytically deeper work on various aspects of the formation, transport pathways, and accumulation of RD in urban, industrial and other areas

The Importance of Surface Layer Parameterization in Modeling of Stable Atmospheric Boundary Layers

The accuracy of prediction of stable atmospheric boundary layers depends on the parameterization of the surface layer which is usually derived from the Monin–Obukhov similarity theory. In this article, several surface-layer models in the format of velocity and potential temperature Deacon numbers are compared with observations from CASES99, Cardington, and Halley datasets. The comparisons were hindered by a large amount of scatter within and among datasets. Tests utilizing R2 demonstrated that the quasi-normal scale elimination (QNSE) theory exhibits the best overall performance. Further proof of this was provided by 1D simulations with the Weather Research and Forecasting (WRF) model

The factors associated with distress following exposure to smoke from an extended coal mine fire

In February 2014, the coalmine adjacent to the Hazelwood Power Station in the Latrobe Valley of Victoria, Australia, caught fire, with residents from the nearby town of Morwell and the wider area exposed to smoke for six weeks. Although there was evidence linking the mine-fire event with psychological distress, no studies have evaluated the degree of distress in relation to the level of smoke exposure. We aimed to investigate the exposure-response relationship between particulate matter 2.5μm or less in diameter (PM2.5) released during the Hazelwood mine fire event and long-term symptoms of posttraumatic distress in the affected community, including the consideration of other key factors. A total of 3,096 Morwell residents, and 960 residents from the largely unexposed comparison community of Sale, were assessed for symptoms of posttraumatic distress 2.5 years after the Hazelwood incident using the Impact of Events Scale-Revised (IES-R). Individual-level PM2.5 exposure was estimated by mapping participants’ self-reported location data on modelled PM2.5 concentrations related to the mine fire. Multivariate linear regression was used to evaluate the exposure-response relationship. Both mean and peak exposure to mine fire-related PM2.5 were found to be associated with participant IES-R scores with an interaction effect between age and mean PM2.5 exposure also identified. Each 10 µg/m3 increase in mean PM2.5 exposure corresponded to a 0.98 increase in IES-R score (95% CI: 0.36 to 1.61), and each 100 µg/m3 increase in peak PM2.5 exposure corresponded to a 0.36 increase (95% CI: 0.06 to 0.67). An age-effect was observed, with the exposure-response association found to be stronger for younger adults. The results suggest that increased exposure to PM2.5 emissions from the Hazelwood mine fire event was associated with higher levels of psychological distress associated with the mine fire and the most pronounced effect was on younger adults living in the affected community

The Ginninderra CH4 and CO2 release experiment : an evaluation of gas detection and quantification techniques

A methane (CH4) and carbon dioxide (CO2) release experiment was held from April to June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. Eight quantification techniques were assessed: three tracer ratio techniques (two mobile); backwards Lagrangian stochastic modelling; forwards Lagrangian stochastic modelling; Lagrangian stochastic (LS) footprint modelling; atmospheric tomography using point and using integrated line sensors. The majority of CH4 estimates were within 20% of the actual CH4 release rate (5.8 g/min), with the tracer ratio technique providing the closest estimate to both the CH4 and CO2 release rates (100 g/min). Once the release rate was known, the majority of revised estimates were within 10% of the actual release rate. The study illustrates the power of measuring the emission rate using multiple simultaneous methods and obtaining an ensemble median or mean. An ensemble approach to estimating the CH4 emission rate proved successful with the ensemble median estimate within 16% for the actual release rate for the blind release experiment and within 2% once the release rate was known. The release also provided an opportunity to assess the effectiveness of stationary and mobile ground and aerial CH4 detection technologies. Sensor detection limits and sampling rates were found to be significant limitations for CH4 and CO2 detection. A hyperspectral imager's capacity to image the CH4 release from 100 m, and a Boreal CH4 laser sensor's ability to track moving targets suggest the future possibility to map gas plumes using a single laser and mobile aerial reflector