Assessing the effectiveness of an urban CO2 monitoring network through the COVID-19 lockdown natural experiment

2021 ◽  
Author(s):  
Jinghui Lian ◽  
Thomas Lauvaux ◽  
Hervé Utard ◽  
Grégoire Broquet ◽  
François-Marie Bréon ◽  
...  
Keyword(s):  
Real Time ◽  
Co2 Emissions ◽  
Transport Model ◽  
Monitoring Network ◽  
Co2 Concentration ◽  
Mitigation Measures ◽  
Potential Contribution ◽  
Activity Data ◽  
Co2 Monitoring ◽  
The City

<p>Quantitative monitoring of CO2 sources and sinks over cities is needed to support the urban adaptation and mitigation measures, but it is a challenging task. The Paris metropolitan area is a highly built-up and densely populated region in France. The two national COVID-19 forced confinements that are 1) effective on March 17th, with a duration of 55 days until May 11th, 2) effective on October 30th, with a duration of 46 days until December 15th provide an opportunity to assess the behaviour and robustness of the dedicated atmospheric inversion system for estimating the city-scale CO2 emissions.</p><p>In this study, the atmospheric Bayesian inversion approach that couples six in-situ continuous CO2 monitoring stations with the WRF-Chem transport model at 1-km horizontal resolutions has been used to quantify the impacts of lockdown on CO<sub>2</sub> emissions for the Paris megacity. The prior emission estimate was from the Origins inventory, a near-real-time dataset of fossil fuel CO2 emissions by sector (transportation, residential, tertiary, industry and sink) at 1km² and hourly resolution recently developed by Origins.earth. Estimates of CO2 emissions were retrieved from the inversion by assimilating CO2 concentration gradients between upwind-downwind stations using a refined configuration of the existing Parisian inversion system developed by Bréon et al. (2015) and Staufer et al. (2016). A set of experiments was performed to assess the sensitivity of the posterior CO2 estimates to the changes in different inversion setups, including the selection of observations, prior flux uncertainties and error correlations. We also analyzed the potential contribution of the expanding CO2 monitoring network, in particular the two newly built urban stations in the city center since 2014, to the inverse modeling systems.</p><p>The optimized CO2 estimates show decreases of around 42% and 25% in anthropogenic CO2 emissions during the first and second lockdowns respectively when compared with the same period in past two years. Both lockdown emission reduction estimates from the inversion are consistent with recent estimates from activity data (resp. 37% and 19%), suggesting that our near-real time monitoring system is able to detect and quantify short-term variations at the whole-city level.</p>

scholarly journals Quantitative evaluation of the uncertainty sources for the modeling of atmospheric CO<sub>2</sub> concentration within and in the vicinity of Paris city

2020 ◽  
Author(s):  
Jinghui Lian ◽  
François-Marie Bréon ◽  
Grégoire Broquet ◽  
Bo Zheng ◽  
Michel Ramonet ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Co2 Emissions ◽  
Large Scale ◽  
Transport Model ◽  
Co2 Concentration ◽  
Inversion Method ◽  
Different Boundary Conditions ◽  
Paris Area ◽  
The City ◽  
Atmospheric Inversion

Abstract. The top-down atmospheric inversion method that couples atmospheric CO2 observations with an atmospheric transport model has been used extensively to quantify CO2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO2 that are of different origins than the targeted CO2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of one-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations and CO2 boundary conditions. The simulated CO2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime observation-model misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Our results suggest three selection criteria for the CO2 data to be assimilated for the inversion of CO2 emissions from Paris (i) discard data that appear as statistical outliers in the model-data misfits which are interpreted as model's deficiencies under complex meteorological conditions; (ii) use only afternoon urban measurements in winter and suburban ones in summer; (iii) test the influence of different boundary conditions in inversions. If possible, using additional observations to constrain the boundary inflow, or using CO2 gradients of upwind-downwind stations, rather than absolute CO2 concentration, as atmospheric inversion inputs.

Simulation experiments for studying an optimal carbon dioxide monitoring network for Osaka, Japan

2020 ◽  
Author(s):  
Takayuki Hayashida ◽  
Tomohiro Oda ◽  
Takashi Machimura ◽  
Takanori Matsui ◽  
Akihiko Kuze ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Monitoring Network ◽  
Co2 Concentration ◽  
Second Phase ◽  
Emission Model ◽  
Geospatial Information ◽  
Observation Network ◽  
Emission Changes ◽  
Carbon Dioxide Co2

&lt;p&gt;We prototype an Observing System Simulation Experiment (OSSE) system for studying an optimal carbon dioxide (CO2) monitoring network in Osaka city, one of the populated cities in Japan (population: 8.8 million).&amp;#160; In the first phase of our project, we built a multi-resolution, spatially-explicit fossil fuel CO2 emissions model to better quantify CO2 emissions with an updated information and detailed geospatial information. &amp;#160;In the second phase, we coupled the emission model to the WRF Chem model, and developed an OSSE capability to study an optimal CO2 observation network for Osaka. &amp;#160;After completing an evaluation of the meteorological fields and emission fields, we have started simulating atmospheric CO2 concentration using possible emission scenarios and examined the emission change detectability by an imaginary ground-based observation networks.&amp;#160; We started from existing observational sites for air quality monitoring sites and the selected suitable sites based on how much useful signals can be obtained.&amp;#160; In order to fully examine the detectability of CO2 emission changes in the presence of potential strong local and inflow biospheric CO2 contributions, we included biospheric fluxes calculated from the BEAMS model. &amp;#160;We have also attempted to calculate the cost for establishing the observational sites.&amp;#160; Our ultimate goal is to help decision makers to design an effective observation network given their emission reduction target as well as the budget constrain.&lt;/p&gt;

scholarly journals Global to local impacts on atmospheric CO2 from the COVID-19 lockdown, biosphere and weather variabilities

2021 ◽  
Author(s):  
Ning Zeng ◽  
Pengfei Han ◽  
Zhiqiang Liu ◽  
Di Liu ◽  
Tomohiro Oda ◽  
...  
Keyword(s):  
Fossil Fuel ◽  
Marine Boundary Layer ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Monitoring Systems ◽  
Positive Anomaly ◽  
Carbon Cycle Model ◽  
The City

Abstract The world-wide lockdown in response to the COVID-19 pandemic in year 2020 led to economic slowdown and large reduction in fossil fuel CO2 emissions, but it is unclear how much it would slow the increasing trend of atmospheric CO2 concentration, the main driver of climate change, and whether this impact can be observed in light of large biosphere and weather variabilities. We used a state-of-the-art atmospheric transport model to simulate CO2, driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show 0.21 ppm decrease in atmospheric column CO2 anomaly in the Northern Hemisphere latitude band 0-45°N (NH45) in March 2020, and an average of 0.14 ppm for the period of February-April 2020, the largest in the last 10 years. A similar decrease was observed by the carbon satellite GOSAT. Using model sensitivity experiments, we further found that COVID and weather variability are the major contributors of this CO2 drawdown, and the biosphere gave a small positive anomaly. Measurements at marine boundary layer stations such as Hawaii exhibits 1-2 ppm anomalies, mostly due to weather and the biosphere. At city scale, on-road CO2 enhancement measured in Beijing shows reduction of 20-30 ppm, consistent with drastically reduced traffic during COVID lockdown. A stepwise drop of 20 ppm at the city-wide lockdown was observed in the city of Chengdu. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signal on the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment. Its impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy and expanded spatiotemporal coverage of our monitoring systems.

scholarly journals Sensitivity to the sources of uncertainties in the modeling of atmospheric CO<sub>2</sub> concentration within and in the vicinity of Paris

2021 ◽  
Vol 21 (13) ◽  
pp. 10707-10726
Author(s):  
Jinghui Lian ◽  
François-Marie Bréon ◽  
Grégoire Broquet ◽  
Thomas Lauvaux ◽  
Bo Zheng ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Co2 Emissions ◽  
Vertical Mixing ◽  
Co2 Concentration ◽  
Atmospheric Modeling ◽  
Inversion Method ◽  
Model Data ◽  
Co2 Concentrations ◽  
Paris Area ◽  
The City

Abstract. The top-down atmospheric inversion method that couples atmospheric CO2 observations with an atmospheric transport model has been used extensively to quantify CO2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO2 that are of different origins than the targeted CO2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of 1-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations, and CO2 boundary conditions. The simulated CO2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime model–data misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Nevertheless, our results demonstrate the potential of our optimal CO2 atmospheric modeling system to be utilized in atmospheric inversions of CO2 emissions over the Paris metropolitan area. We evaluated the model performances in terms of wind, vertical mixing, and CO2 model–data mismatches, and we developed a filtering algorithm for outliers due to local contamination and unfavorable meteorological conditions. Analysis of model–data misfit indicates that future inversions at the mesoscale should only use afternoon urban CO2 measurements in winter and suburban measurements in summer. Finally, we determined that errors related to CO2 boundary conditions can be overcome by including distant background observations to constrain the boundary inflow or by assimilating CO2 gradients of upwind–downwind stations rather than by assimilating absolute CO2 concentrations.

scholarly journals An attempt at estimating Paris area CO<sub>2</sub> emissions from atmospheric concentration measurements

2015 ◽  
Vol 15 (4) ◽  
pp. 1707-1724 ◽  
Author(s):  
F. M. Bréon ◽  
G. Broquet ◽  
V. Puygrenier ◽  
F. Chevallier ◽  
I. Xueref-Remy ◽  
...  
Keyword(s):  
Urban Area ◽  
Co2 Emissions ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Atmospheric Concentration ◽  
Paris Area ◽  
Direct Model ◽  
Concentration Measurements

Abstract. Atmospheric concentration measurements are used to adjust the daily to monthly budget of fossil fuel CO2 emissions of the Paris urban area from the prior estimates established by the Airparif local air quality agency. Five atmospheric monitoring sites are available, including one at the top of the Eiffel Tower. The atmospheric inversion is based on a Bayesian approach, and relies on an atmospheric transport model with a spatial resolution of 2 km with boundary conditions from a global coarse grid transport model. The inversion adjusts prior knowledge about the anthropogenic and biogenic CO2 fluxes from the Airparif inventory and an ecosystem model, respectively, with corrections at a temporal resolution of 6 h, while keeping the spatial distribution from the emission inventory. These corrections are based on assumptions regarding the temporal autocorrelation of prior emissions uncertainties within the daily cycle, and from day to day. The comparison of the measurements against the atmospheric transport simulation driven by the a priori CO2 surface fluxes shows significant differences upwind of the Paris urban area, which suggests a large and uncertain contribution from distant sources and sinks to the CO2 concentration variability. This contribution advocates that the inversion should aim at minimising model–data misfits in upwind–downwind gradients rather than misfits in mole fractions at individual sites. Another conclusion of the direct model–measurement comparison is that the CO2 variability at the top of the Eiffel Tower is large and poorly represented by the model for most wind speeds and directions. The model's inability to reproduce the CO2 variability at the heart of the city makes such measurements ill-suited for the inversion. This and the need to constrain the budgets for the whole city suggests the assimilation of upwind–downwind mole fraction gradients between sites at the edge of the urban area only. The inversion significantly improves the agreement between measured and modelled concentration gradients. Realistic emissions are retrieved for two 30-day periods and suggest a significant overestimate by the AirParif inventory. Similar inversions over longer periods are necessary for a proper evaluation of the optimised CO2 emissions against independent data.

Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

2018 ◽  
Vol 18 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Georgiana Grigoraș ◽  
Bogdan Urițescu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Hot Spot ◽  
Monitoring Network ◽  
Residential Areas ◽  
Hotspot Analysis ◽  
The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

scholarly journals A Remote Monitoring System of Logistics Carrier Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 4
Author(s):  
Wang Weidong
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Monitoring Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Remote Monitoring System ◽  
Monitoring Center

To improve the efficiency of the remote monitoring system for logistics transportation, we proposed a remote monitoring system based on wireless sensor network and GPRS communication. The system can collect information from the wireless sensor network and transmit the information to the ZigBee interpreter. The monitoring system mainly includes the following parts: Car terminal, GPRS transmission network and monitoring center. Car terminal mainly consists by the Zigbee microcontroller and peripherals, wireless sensor nodes, RFID reader, GPRS wireless communication module composed of a micro-wireless monitoring network. The information collected by the sensor communicates through the GPRS and the monitoring center on the network coordinator, sends the collected information to the monitoring center, and the monitoring center realizes the information of the logistics vehicle in real time. The system has high applicability, meets the design requirements in the real-time acquisition and information transmission of the information of the logistics transport vehicles and goods, and realizes the function of remote monitoring.

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

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