Methods for now-casting adverse weather conditions with the potential to cause disruption to aircraft landings often make use of real-time measurements at high temporal resolution. This paper describes processing methodologies developed to derive meteorological parameters from such measurements recorded in the vicinity of Hong Kong International Airport, specifically a radiometer in King’s Park, a wind profiler and surface anemometer on Cheung Chau Island and weather buoys in the Pearl River estuary. These parameters are suitable for use as input to a now-casting application of the computationally efficient airflow model, FLOWSTAR, which has previously been shown to predict mountain waves generated by flow over Lantau Island to the southeast of the airport. Radiosonde data from King’s Park have been used to test the radiometer processing method; the novel approach of using minimum and maximum potential temperature deviations from a series of height-dependent linear profiles to derive radiometer inversion layer parameters generates data that compares well with values derived from corresponding radiosonde profiles. Mountain wave strength depends on the magnitude of wind speed in the inversion layer; wind profiler data can be used to estimate typical and maximum wind speeds and associated wind directions using estimates of inversion layer depth derived from the radiometer data. With estimates of surface sensible heat flux appropriate for the airport’s coastal location calculated using a marine boundary layer scheme, a dataset of meteorological parameters at 20-min resolution has been derived for input into the FLOWSTAR model. The combination of automated meteorological data processing methods and flow field modelling has the potential to form part of a now-casting system for determining strong wind shear conditions at the airport.
Abstract. Ambient air pollution poses a major global public health risk. Lower-cost air quality sensors (LCSs) are increasingly being explored as a tool to understand local air pollution problems and develop effective solutions. A barrier to LCS adoption is potentially larger measurement uncertainty compared to reference measurement technology. The technical performance of various LCSs has been tested in laboratory and field environments, and a growing body of literature on uses of LCSs primarily focuses on proof-of-concept deployments. However, few studies have demonstrated the implications of LCS measurement uncertainties on a sensor network's ability to assess spatiotemporal patterns of local air pollution. Here, we present results from a 2-year deployment of 100 stationary electrochemical nitrogen dioxide (NO2) LCSs across Greater London as part of the Breathe London pilot project (BL). We evaluated sensor performance using collocations with reference instruments, estimating ∼ 35 % average uncertainty (root mean square error) in the calibrated LCSs, and identified infrequent, multi-week periods of poorer performance and high bias during summer months. We analyzed BL data to generate insights about London's air pollution, including long-term concentration trends, diurnal and day-of-week patterns, and profiles of elevated concentrations during regional pollution episodes. These findings were validated against measurements from an extensive reference network, demonstrating the BL network's ability to generate robust information about London's air pollution. In cases where the BL network did not effectively capture features that the reference network measured, ongoing collocations of representative sensors often provided evidence of irregularities in sensor performance, demonstrating how, in the absence of an extensive reference network, project-long collocations could enable characterization and mitigation of network-wide sensor uncertainties. The conclusions are restricted to the specific sensors used for this study, but the results give direction to LCS users by demonstrating the kinds of air pollution insights possible from LCS networks and provide a blueprint for future LCS projects to manage and evaluate uncertainties when collecting, analyzing, and interpreting data.
Abstract. Ambient air pollution poses a major global public health risk. Lower-cost air quality sensors (LCS) are increasingly being explored as a tool to understand local air pollution problems and develop effective solutions. A barrier to LCS adoption is potentially larger measurement uncertainty compared to reference measurement technology. The technical performance of various LCS has been tested in laboratory and field environments, and a growing literature on uses of LCS primarily focuses on proof-of-concept deployments. However, few studies have demonstrated the implications of LCS measurement uncertainties on a sensor network’s ability to assess spatiotemporal patterns of local air pollution. Here, we present results from a 2-year deployment of 100 stationary electrochemical nitrogen dioxide (NO2) LCS across Greater London as part of the Breathe London pilot project (BL). We evaluated sensor performance using collocations with reference instruments, estimating ~35 % average uncertainty (root-mean-square error) of the calibrated LCS, and identified infrequent, multi-week periods of poorer performance and high bias during summer months. We analyzed BL data to generate insights about London’s air pollution, including long-term concentration trends, diurnal and day-of-week patterns, and profiles of elevated concentrations during regional pollution episodes. These findings were validated against measurements from an extensive reference network, demonstrating the BL network’s ability to generate robust information about London’s air pollution. In cases where the BL network did not effectively capture features that the reference network measured, ongoing collocations of representative sensors often provided evidence of irregularities in sensor performance, demonstrating how, in the absence of an extensive reference network, project-long collocations could enable characterization and mitigation of network-wide sensor uncertainties. The conclusions are restricted to the specific sensors used for this study, but the results give direction to LCS users by demonstrating the kinds of air pollution insights possible from LCS networks and provide a blueprint for future LCS projects to manage and evaluate uncertainties when collecting, analyzing and interpreting data.
<p>Major cities such as London are increasingly becoming targets for reducing greenhouse gas emissions by policy makers. This is due in part to their higher rate of emissions compared to more rural areas, but also due to the political powers of city level government. To ensure that emission reduction policies are successful, policy makers require accurate knowledge of how emissions change over time.</p><p>The London Greenhouse Gas Project aims to provide top-down emission estimates for London, adding a London measurement network to expand upon the UK&#8217;s existing national top-down measurement infrastructure. The national network has proved useful in contributing to the UK&#8217;s national emission reports, and the new local network will provide useful data targeted to London&#8217;s policy makers.</p><p>A series of in-situ atmospheric concentration instruments are being installed across the city and will be used to estimate London&#8217;s emissions of methane initially, with carbon dioxide emissions to follow. A medium-density urban network provides challenges in instrument calibration and siting, as well as the development of new modelling approaches to capture the urban environment and link the measurements to policy-relevant emissions estimates. There are also opportunities to link with remote observations of London, including satellite and ground-based FTIR instruments. We present considerations of setting up the new network, and results from the initial instrument installation and model development.</p>
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