Reducing uncertainty in emission estimates using perturbed emissions ensembles and novel observations: A focus on Beijing

2020 ◽  
Author(s):  
Le Yuan ◽  
David Carruthers ◽  
Christina Hood ◽  
Roderic L. Jones ◽  
Olalekan A.M. Popoola ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Dispersion Model ◽  
Low Cost ◽  
Time Lag ◽  
Three Dimensional ◽  
Expert Elicitation ◽  
Control Measures ◽  
Emissions Inventory ◽  
Emission Changes

<p>The time lag between the occurrence of emissions and the compilation of an inventory is inevitable. When an emissions inventory is used to simulate air quality, uncertainties in the emissions are propagated into uncertainties in the modelled pollutant concentrations. Such uncertainties can be particularly high in regions undergoing rapid emission changes. Beijing, for instance, has implemented a series of pollution control measures over the past several years and various studies have confirmed significant decreases in the emissions of pollutants such as CO and NO<sub>X</sub>. Hence, it is crucial to quantify and constrain the uncertainties in existing emission estimates for this region.</p><p>We sample the uncertainties in an emissions inventory for Beijing using a high-resolution advanced Gaussian dispersion model with perturbed emissions ensembles (PEEs), and constrain these uncertainties using a comprehensive set of in situ observations, including vertically resolved measurements made from a tower in central Beijing using low-cost sensors. We first construct a PEE by varying key emission parameters including source sectors, vertical and diurnal profiles within their uncertainty ranges estimated through expert elicitation. By removing the baseline contribution to the concentrations, we are able to evaluate the performance of the PEE in simulating the local signal. Based on knowledge gained from the initial PEE, we design a second PEE with optimised uncertainty ranges with which we constrain the uncertainties in the base emission estimates.</p><p>Our study shows the applicability of perturbed emissions ensembles and high-resolution, three-dimensional observations in systematically sampling and constraining emission uncertainties. This method has wide implications for air quality modelling, particularly in regions with rapid emission changes or for studies in which emissions inventories are out-dated.</p>

scholarly journals Simulation and measurement of air quality in the traffic congestion area

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Shin Yu ◽  
Chang Tang Chang ◽  
Chih Ming Ma
Keyword(s):  
Air Quality ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Reference Model ◽  
Dispersion Model ◽  
Fluid Model ◽  
Three Dimensional ◽  
Vehicle Speed ◽  
Management Plans ◽  
Dimensional Distribution

AbstractThe traffic congestion in the Hsuehshan tunnel and at the Toucheng interchange has led to traffic-related air pollution with increasing concern. To ensure the authenticity of our simulation, the concentration of the last 150 m in Hsuehshan tunnel was simulated using the computational fluid dynamics fluid model. The air quality at the Toucheng interchange along a 2 km length highway was simulated using the California Line Source Dispersion Model. The differences in air quality between rush hours and normal traffic conditions were also investigated. An unmanned aerial vehicle (UAV) with installed PM2.5 sensors was developed to obtain the three-dimensional distribution of pollutants. On different roads, during the weekend, the concentrations of pollutants such as SOx, CO, NO, and PM2.5 were observed to be in the range of 0.003–0.008, 7.5–15, 1.5–2.5 ppm, and 40–80 μg m− 3, respectively. On weekdays, the vehicle speed and the natural wind were 60 km h− 1 and 2.0 m s− 1, respectively. On weekdays, the SOx, CO, NO, and PM2.5 concentrations were found to be in the range of 0.002–0.003, 3–9, 0.7–1.8 ppm, and 35–50 μg m− 3, respectively. The UAV was used to verify that the PM2.5 concentrations of vertical changes at heights of 9.0, 7.0, 5.0, and 3.0 m were 45–48, 30–35, 25–30, and 50–52 μg m− 3, respectively. In addition, the predicted PM2.5 concentrations were 40–45, 25–30, 45–48, and 45–50 μg m− 3 on weekdays. These results provide a reference model for environmental impact assessments of long tunnels and traffic jam-prone areas. These models and data are useful for transportation planners in the context of creating traffic management plans.

Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

2021 ◽  
Author(s):  
Adrian Wenzel ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Sebastian T. Thekkekara ◽  
Daniel Zollitsch ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Sensor Network ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Small Scale ◽  
Urban Air

<p>Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution – especially with respect to spatial and temporally variability – measurement data with high spatial and temporal resolution are critical.</p><p>Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O<sub>3</sub>) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].</p><p>After having conducted a measurement campaign in 2016 to create a high-resolution NO<sub>2</sub> concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O<sub>3</sub> and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.</p><p> </p><p>[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018</p><p>[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018</p><p>[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, 2020</p><p>[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020</p>

scholarly journals Numerical analysis of the impact of agricultural emissions on PM<sub>2.5</sub> in China using a high-resolution ammonia emissions inventory

2020 ◽  
Author(s):  
Xiao Han ◽  
Lingyun Zhu ◽  
Mingxu Liu ◽  
Yu Song ◽  
Meigen Zhang
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Source Apportionment ◽  
Mass Concentration ◽  
Atmospheric Modeling ◽  
Central China ◽  
High Mass ◽  
Emissions Inventory ◽  
Aerosol Mass Concentration ◽  
The Impact

Abstract. China is one of the largest agricultural countries in the world. The NH3 emissions from agricultural activities in China significantly affect regional air quality and horizontal visibility. To reliably estimate the influence of NH3 on agriculture, a high-resolution agricultural NH3 emissions inventory, compiled with a 1 km × 1 km horizontal resolution, was applied to calculate the NH3 mass burden in China. The key emission factors of this inventory were enhanced by considering the results of many native experiments, and the activity data of spatial and temporal information were updated using statistical data from 2015. Fertilizer and husbandry, as well as farmland ecosystems, livestock waste, crop residue burning, fuel wood combustion, and other NH3 emission sources were included in the inventory. Furthermore, a source apportionment tool, ISAM (Integrated Source Apportionment Method), coupled with the air quality modeling system RAMS-CMAQ (Regional Atmospheric Modeling System and Community Multiscale Air Quality), was applied to capture the contribution of NH3 emitted from total agriculture (Tagr) in China. The aerosol mass concentration in 2015 was simulated, and the results showed that a high mass concentration of NH3, which exceeded 10 μg m−3, appeared mainly in the North China Plain (NCP), Central China (CNC), the Yangtz River Delta (YRD), and the Sichan Basin (SCB), and the annual average contribution of Tagr NH3 to PM2.5 mass burden in China was 14–18 %. Specific to the PM2.5 components, Tagr NH3 provided a major contribution to ammonium formation (87.6 %) but a tiny contribution to sulfate (2.2 %). In addition, several brute-force sensitivity tests were conducted to estimate the impact of Tagr NH3 emissions reduction on the PM2.5 mass burden. Compared with the results of ISAM, it was found that even though the Tagr NH3 only contributed 10.1 % of nitrate under current emissions scenarios, the reduction of nitrate could reach 98.8 % upon removal of the Tagr NH3 emissions. The main reason for this deviation could be that the NH3 contribution to nitrate is small under rich NH3 conditions and large in poor NH3 environments. Thus, the influence of NH3 on nitrate formation could be enhanced with the decrease of ambient NH3 mass concentration.

Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

2020 ◽  
Author(s):  
Philipp Schneider ◽  
Nuria Castell ◽  
Paul Hamer ◽  
Sam-Erik Walker ◽  
Alena Bartonova
Keyword(s):  
Sensor Networks ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Low Cost ◽  
Urban Air Quality ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Model ◽  
Quality Model

&lt;p&gt;One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM&lt;sub&gt;2.5&lt;/sub&gt; observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.&lt;/p&gt;&lt;p&gt;In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.&lt;/p&gt;&lt;p&gt;While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.&lt;/p&gt;

Study of Microscale Three-Dimensional Printing Using Near-Field Melt Electrospinning

2017 ◽  
Author(s):  
Xiangyu You ◽  
Chengcong Ye ◽  
Ping Guo
Keyword(s):  
High Resolution ◽  
3D Printing ◽  
Low Cost ◽  
Near Field ◽  
Three Dimensional ◽  
Thin Wall ◽  
Direct Writing ◽  
Melt Electrospinning ◽  
Wall Structures ◽  
Jet Trajectory

Three-dimensional (3D) printing of microscale structures with high resolution (sub-micron) and low cost is still a challenging work for the existing 3D printing techniques. Here we report a direct writing process via near-field melt electrospinning to achieve microscale printing of single filament wall structures. The process allows continuous direct writing due to the linear and stable jet trajectory in the electric near-field. The layer-by-later stacking of fibers, or self-assembly effect, is attributed to the attraction force from the molten deposited fibers and accumulated negative charges. We demonstrated successful printing of various 3D thin wall structures (freestanding single walls, double walls, annular walls, star-shaped structures, and curved wall structures) with a minimal wall thickness less than 5 μm. By optimizing the process parameters of near-field melt electrospinning (electric field strength, collector moving speed, and needle-to-collector distance), ultrafine poly (ε-caprolactone) (PCL) fibers have been stably generated and precisely stacked and fused into 3D thin-wall structures with an aspect ratio of more than 60. It is envisioned that the near-field melt electrospinning can be transformed into a viable high-resolution and low-cost microscale 3D printing technology.

scholarly journals Unravelling earth flow dynamics with 3-D time series derived from UAV-SfM models

2017 ◽  
Vol 5 (4) ◽  
pp. 791-806 ◽  
Author(s):  
François Clapuyt ◽  
Veerle Vanacker ◽  
Fritz Schlunegger ◽  
Kristof Van Oost
Keyword(s):  
High Resolution ◽  
Slope Failure ◽  
Drainage Basin ◽  
Time Lag ◽  
Mass Movement ◽  
Three Dimensional ◽  
Sediment Budget ◽  
Swiss Alps ◽  
Surface Displacements ◽  
Sediment Redistribution

Abstract. Accurately assessing geo-hazards and quantifying landslide risks in mountainous environments are gaining importance in the context of the ongoing global warming. For an in-depth understanding of slope failure mechanisms, accurate monitoring of the mass movement topography at high spatial and temporal resolutions remains essential. The choice of the acquisition framework for high-resolution topographic reconstructions will mainly result from the trade-off between the spatial resolution needed and the extent of the study area. Recent advances in the development of unmanned aerial vehicle (UAV)-based image acquisition combined with the structure-from-motion (SfM) algorithm for three-dimensional (3-D) reconstruction make the UAV-SfM framework a competitive alternative to other high-resolution topographic techniques. In this study, we aim at gaining in-depth knowledge of the Schimbrig earthflow located in the foothills of the Central Swiss Alps by monitoring ground surface displacements at very high spatial and temporal resolution using the efficiency of the UAV-SfM framework. We produced distinct topographic datasets for three acquisition dates between 2013 and 2015 in order to conduct a comprehensive 3-D analysis of the landslide. Therefore, we computed (1) the sediment budget of the hillslope, and (2) the horizontal and (3) the three-dimensional surface displacements. The multitemporal UAV-SfM based topographic reconstructions allowed us to quantify rates of sediment redistribution and surface movements. Our data show that the Schimbrig earthflow is very active, with mean annual horizontal displacement ranging between 6 and 9 m. Combination and careful interpretation of high-resolution topographic analyses reveal the internal mechanisms of the earthflow and its complex rotational structure. In addition to variation in horizontal surface movements through time, we interestingly showed that the configuration of nested rotational units changes through time. Although there are major changes in the internal structure of the earthflow in the 2013–2015 period, the sediment budget of the drainage basin is nearly in equilibrium. As a consequence, our data show that the time lag between sediment mobilization by landslides and enhanced sediment fluxes in the river network can be considerable.

scholarly journals Evaluating galactic habitability using high-resolution cosmological simulations of galaxy formation

2016 ◽  
Vol 16 (1) ◽  
pp. 60-73 ◽  
Author(s):  
Duncan Forgan ◽  
Pratika Dayal ◽  
Charles Cockell ◽  
Noam Libeskind
Keyword(s):  
High Resolution ◽  
Time Evolution ◽  
Galaxy Formation ◽  
Time Lag ◽  
Three Dimensional ◽  
Vital Role ◽  
Galactic Centre ◽  
Cosmological Simulations ◽  
Tidal Streams ◽  
Galaxy Assembly

AbstractWe present the first model that couples high-resolution simulations of the formation of local group galaxies with calculations of the galactic habitable zone (GHZ), a region of space which has sufficient metallicity to form terrestrial planets without being subject to hazardous radiation. These simulations allow us to make substantial progress in mapping out the asymmetric three-dimensional GHZ and its time evolution for the Milky Way (MW) and Triangulum (M33) galaxies, as opposed to works that generally assume an azimuthally symmetric GHZ. Applying typical habitability metrics to MW and M33, we find that while a large number of habitable planets exist as close as a few kiloparsecs from the galactic centre, the probability of individual planetary systems being habitable rises as one approaches the edge of the stellar disc. Tidal streams and satellite galaxies also appear to be fertile grounds for habitable planet formation. In short, we find that both galaxies arrive at similar GHZs by different evolutionary paths, as measured by the first and third quartiles of surviving biospheres. For the MW, this interquartile range begins as a narrow band at large radii, expanding to encompass much of the Galaxy at intermediate times before settling at a range of 2–13 kpc. In the case of M33, the opposite behaviour occurs – the initial and final interquartile ranges are quite similar, showing gradual evolution. This suggests that Galaxy assembly history strongly influences the time evolution of the GHZ, which will affect the relative time lag between biospheres in different galactic locations. We end by noting the caveats involved in such studies and demonstrate that high-resolution cosmological simulations will play a vital role in understanding habitability on galactic scales, provided that these simulations accurately resolve chemical evolution.

Study of Microscale Three-Dimensional Printing Using Near-Field Melt Electrospinning

2017 ◽  
Vol 5 (4) ◽  
Author(s):  
Xiangyu You ◽  
Chengcong Ye ◽  
Ping Guo
Keyword(s):  
High Resolution ◽  
3D Printing ◽  
Low Cost ◽  
Near Field ◽  
Three Dimensional ◽  
Thin Wall ◽  
Direct Writing ◽  
Melt Electrospinning ◽  
Wall Structures ◽  
Jet Trajectory

Three-dimensional (3D) printing of microscale structures with high-resolution (submicron) and low-cost is still a challenging work for the existing 3D printing techniques. Here, we report a direct writing process via near-field melt electrospinning (NFME) to achieve microscale printing of single filament wall structures. The process allows continuous direct writing due to the linear and stable jet trajectory in the electric near field. The layer-by-layer stacking of fibers, or self-assembly effect, is attributed to the attraction force from the molten deposited fibers and accumulated negative charges. We demonstrated successful printing of various 3D thin-wall structures with a minimal wall thickness less than 5 μm. By optimizing the process parameters of NFME, ultrafine poly (ε-caprolactone) (PCL) fibers have been stably generated and precisely stacked and fused into 3D thin-wall structures with an aspect ratio of more than 60. It is envisioned that the NFME can be transformed into a viable high-resolution and low-cost microscale 3D printing technology.

scholarly journals Using Task Farming to Optimise a Street-Scale Resolution Air Quality Model of the West Midlands (UK)

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 983
Author(s):  
Jian Zhong ◽  
Christina Hood ◽  
Kate Johnson ◽  
Jenny Stocker ◽  
Jonathan Handley ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Dispersion Model ◽  
Full Range ◽  
Measurement Data ◽  
Air Quality Model ◽  
The West ◽  
Model Domain ◽  
West Midlands ◽  
Large Model

High resolution air quality models combining emissions, chemical processes, dispersion and dynamical treatments are necessary to develop effective policies for clean air in urban environments, but can have high computational demand. We demonstrate the application of task farming to reduce runtime for ADMS-Urban, a quasi-Gaussian plume air dispersion model. The model represents the full range of source types (point, road and grid sources) occurring in an urban area at high resolution. Here, we implement and evaluate the option to automatically split up a large model domain into smaller sub-regions, each of which can then be executed concurrently on multiple cores of a HPC or across a PC network, a technique known as task farming. The approach has been tested for a large model domain covering the West Midlands, UK (902 km2), as part of modelling work in the WM-Air (West Midlands Air Quality Improvement Programme) project. Compared to the measurement data, overall, the model performs well. Air quality maps for annual/subset averages and percentiles are generated. For this air quality modelling application of task farming, the optimisation process has reduced weeks of model execution time to approximately 35 h for a single model configuration of annual calculations.

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