Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

Carruthers, David; Stidworthy, Amy; Clarke, Daniel; Dicks, Jo; Jones, R. L.; Leslie, Ian; Popoola, Olalekan; Seaton, Martin

doi:10.1504/ijep.2019.104878

Cited by 6 publications

(4 citation statements)

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“…The spatial resolution is 25 m × 25 m and covers Marseille and its surroundings (see also [37] and references therein). The numerical model uses a combination of the regional chemistry transport model CHIMERE [38,39] and the street-level dispersion model ADMS-URBAN [40,41]. The methodology used for the coupling can be found in Hood et al [42].…”

Section: Air Quality Simulationsmentioning

confidence: 99%

Urban-Scale NO2 Prediction with Sensors Aboard Bicycles: A Comparison of Statistical Methods Using Synthetic Observations

et al. 2020

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Mobile devices for city-scale air quality monitoring is receiving increasing attention due to the advent of low-cost and miniaturized sensors. Mobility and crowdsensing have emerged as a new means to investigate the ambient air quality in urban areas. However, the design of the network (e.g., number of sensors per unit area) and the scientific interpretation of collected data with an ad hoc method are still challenging. In this paper, we focus on the use of a fleet of private bicycles to monitor NO2 concentrations in the city of Marseille, France. The study is based on synthetic observations generated by means of a regional air quality simulation system at a spatial resolution of 25 m × 25 m and simulated bike trips that are randomly generated in the city. The bike trips correspond to a maximum of 4500 bike commuters and are generated using a web-based navigation service. Simulated bike tracks are validated using available statistics on bike counts. Each bike track is associated with the along-track corresponding NO2 concentrations collected from the air quality simulations and physical features on the ground collected from Open Street Map. Spatialization of the information collected aboard the bikes is tested by using three different algorithms: kriging, land-use regression (LUR) and neural network (NN). LUR and NN show that the fleet can be limited to below 100 bikes while the performance of kriging is steadily increasing with the number of bikes. Increasing the sample distance above 200 m also impairs the citywide prediction of simulated NO2 concentrations.

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Section: Air Quality Simulationsmentioning

confidence: 99%

Urban-Scale NO2 Prediction with Sensors Aboard Bicycles: A Comparison of Statistical Methods Using Synthetic Observations

et al. 2020

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“…To expand the small footprint areas associated with tower-based measurements, measurements from mobile platforms such as towers on vehicles (Moore et al 2009) and aircrafts (Vaughan et al 2016) have been used to obtain urban-scale estimates. Another key approach is to infer emission strengths from ambient pollutant concentrations (Tang et al 2013;Carruthers et al 2019) but more commonly satellite retrievals of total column quantities due to their wider spatiotemporal coverage (Wang et al 2010(Wang et al , 2015Liu et al 2016). Statistical inversion techniques span complex fitting algorithms and mass balance box modelling (de Foy et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing

Yuan

Popoola

Hood

et al. 2021

Air Qual Atmos Health

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The reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×105 Mg in 2016) and a minimum 61% decrease in residential sector emissions (<3.5×105 Mg in 2016) within the study area. We also provide evidence that the night-time fraction of traffic sources in 2016 was higher than that in the 2013 emissions inventory. This study shows the applicability of PEEs and high-resolution observations in providing timely updates of emission estimates by source sector.

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“…Case studies have demonstrated the potential for LCS networks to provide data insights about a local air pollution environment, including characterizing spatiotemporal trends in ambient air quality (Castell et al, 2018;Caubel et al, 2019;Mead et al, 2013;Pope et al, 2018;Popoola et al, 2018) and improving air quality models through data fusion or assimilation (Bi et al, 2020;Carruthers et al, 2019;Gupta et al, 2018;Lopez-Restrepo et al, 2021). While previous LCS deployments often consider uncertainty of individual sensors relative to a reference instrument, we are unaware of network deployments where the spatiotemporal observations have been directly compared to results from a reference network.…”

Section: Introductionmentioning

confidence: 99%

Evaluating uncertainty in sensor networks for urban air pollution insights

Peters

Popoola

Jones

et al. 2021

Preprint

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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.

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Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

Cited by 6 publications

References 0 publications

Urban-Scale NO2 Prediction with Sensors Aboard Bicycles: A Comparison of Statistical Methods Using Synthetic Observations

Urban-Scale NO2 Prediction with Sensors Aboard Bicycles: A Comparison of Statistical Methods Using Synthetic Observations

Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing

Evaluating uncertainty in sensor networks for urban air pollution insights

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