Evaluating the influence of COVID-19 pandemic on NO2 concentration variation in selected regions in China using TROPOMI data, surface measurements and modeling approaches

2021 ◽  
Author(s):  
Zikang Jia ◽  
Yijia Yuan ◽  
Qianyu Pan ◽  
Jianbing Jin ◽  
Song Gao
Keyword(s):  
Machine Learning ◽  
Transport Model ◽  
Control Measures ◽  
Machine Learning Techniques ◽  
Recent Analysis ◽  
Chemical Transport Model ◽  
Ozone Monitoring Instrument ◽  
Surface Measurements ◽  
And Control ◽  
The Impact

<p>During the COVID-19 pandemic outbreak at the beginning of 2020, many Chinese urban agglomerations experienced noticeable air quality improvement. For example, recent analysis of surface measurements suggested that the concentration of NO<sub>2</sub> decreased by on average 30% during the pandemic lockdown period in China in 2020 compared to 2019, although how much of this reduction is due to the pandemic or other factors (such as weather variation) is uncertain. We apply TROPOMI (Tropospheric Ozone Monitoring Instrument) NO<sub>2</sub> Level 2 data (converted to Level 3 data) to analyzing the spatial and temporal evolution of NO<sub>2</sub> in major Chinese city clusters including Jing-Jin-Ji and Yantze River Delta. These observational results are compared with monitoring station data, as well as predicted results from machine learning techniques and a chemical transport model (SILAM), taking meteorological factors into account. We then evaluate the impact of COVID-19 and lockdown measures on the concentration of NO<sub>2</sub> comprehensively. For example, initial results indicate the NO<sub>2</sub> concentration in Shanghai area decreased by about 37% during late January to early March in 2020, comparing the prediction by a machine learning technique (random forest) and the observed surface data, partly due to the pandemic control measures. It is expected the COVID-19 pandemic would be a long-term challenge accompanying the human development. Based on these findings, relevant mechanism of NO<sub>2</sub> pollution and control, affected by the pandemic and periodic lockdown measures in China, will be discussed.</p>

scholarly journals Combined assimilation of IASI and MLS observations to constrain tropospheric and stratospheric ozone in a global chemical transport model

2013 ◽  
Vol 13 (8) ◽  
pp. 21455-21505
Author(s):  
E. Emili ◽  
B. Barret ◽  
S. Massart ◽  
E. Le Flochmoen ◽  
A. Piacentini ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Radiative Forcing ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Chemical Transport ◽  
Free Troposphere ◽  
Chemical Transport Model ◽  
Ozone Monitoring Instrument ◽  
Global Chemical Transport Model ◽  
The Impact

Abstract. Accurate and temporally resolved fields of free-troposphere ozone are of major importance to quantify the intercontinental transport of pollution and the ozone radiative forcing. In this study we examine the impact of assimilating ozone observations from the Microwave Limb Sounder (MLS) and the Infrared Atmospheric Sounding Interferometer (IASI) in a global chemical transport model (MOdèle de Chimie Atmosphérique à Grande Échelle, MOCAGE). The assimilation of the two instruments is performed by means of a variational algorithm (4-D-VAR) and allows to constrain stratospheric and tropospheric ozone simultaneously. The analysis is first computed for the months of August and November 2008 and validated against ozone-sondes measurements to verify the presence of observations and model biases. It is found that the IASI Tropospheric Ozone Column (TOC, 1000–225 hPa) should be bias-corrected prior to assimilation and MLS lowermost level (215 hPa) excluded from the analysis. Furthermore, a longer analysis of 6 months (July–August 2008) showed that the combined assimilation of MLS and IASI is able to globally reduce the uncertainty (Root Mean Square Error, RMSE) of the modeled ozone columns from 30% to 15% in the Upper-Troposphere/Lower-Stratosphere (UTLS, 70–225 hPa) and from 25% to 20% in the free troposphere. The positive effect of assimilating IASI tropospheric observations is very significant at low latitudes (30° S–30° N), whereas it is not demonstrated at higher latitudes. Results are confirmed by a comparison with additional ozone datasets like the Measurements of OZone and wAter vapour by aIrbus in-service airCraft (MOZAIC) data, the Ozone Monitoring Instrument (OMI) total ozone columns and several high-altitude surface measurements. Finally, the analysis is found to be little sensitive to the assimilation parameters and the model chemical scheme, due to the high frequency of satellite observations compared to the average life-time of free-troposphere/low-stratosphere ozone.

scholarly journals Integration of remote sensing data and surface observations to estimate the impact of the russian wildfires over Europe and Asia during August 2010

2011 ◽  
Vol 8 (4) ◽  
pp. 7741-7790 ◽  
Author(s):  
L. Mei ◽  
Y. Xue ◽  
G. de Leeuw ◽  
J. Guang ◽  
Y. Wang ◽  
...  
Keyword(s):  
Transport Model ◽  
Model Simulation ◽  
Atmospheric Environment ◽  
The Arctic ◽  
Negative Consequences ◽  
Chemical Transport Model ◽  
Ozone Monitoring Instrument ◽  
Retrieval Models ◽  
Hysplit Model ◽  
The Impact

Abstract. A series of wildfires broke out in western Russia starting in late July of 2010. Harmful particulates and gases released into the local Russian atmosphere have been reported, as have possible negative consequences for the global atmosphere. In this study, an extremely hazy area and its transport trajectory on Russian wildfires were analysed using aerosol optical depth (AOD) images retrieved via the synergy method from Moderate Resolution Imaging Spectroradiometer (MODIS) data. In addition, we used trace gases (NO2 and SO2) and CO2 products measured using Ozone Monitoring Instrument (OMI) data, vertical distribution of AOD data retrieved from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the mass trajectory analyses, synoptic maps from a HYSPLIT model simulation and ground-based data, including AERONET (both AOD and Ångström exponent) data and PM2.5. First, an Optimal Smoothing (OS) scheme was used to develop more precise and reliable AOD data based on multiple competing predictions made using several AOD retrieval models; then, integrated AOD and PM2.5 data were related using a chemical transport model (GEOS-Chem), and the integrated AOD and visibility data were related using a 6S model. The results show that the PM2.5 concentration is 3–5 times the normal amount based on both satellite data and in situ values with peak daily mean concentrations of approximately 500 μg m−3. Also, the visibility of many parts of Russia, even Moscow, was less than 100 m; in some areas, the visibility was less than 50 m. Additionally, the possible impact on neighbouring countries due to the long-transport effect was also analysed during 31 July and 15 August 2010. A comparison of the satellite aerosol products and ground observations from the neighbouring countries suggests that wildfires in western Russian have had little impact on most European and Asian countries, the exceptions being Finland, Estonia, Ukraine and Kyrgyzstan. However, a possible impact on the Arctic region was also identified; such an effect would have a serious influence on the polar atmospheric environment and on animals such as polar bears.

scholarly journals Combined assimilation of IASI and MLS observations to constrain tropospheric and stratospheric ozone in a global chemical transport model

2014 ◽  
Vol 14 (1) ◽  
pp. 177-198 ◽  
Author(s):  
E. Emili ◽  
B. Barret ◽  
S. Massart ◽  
E. Le Flochmoen ◽  
A. Piacentini ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Radiative Forcing ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Ozone Monitoring Instrument ◽  
Intercontinental Transport ◽  
Global Chemical Transport Model ◽  
The Impact

Abstract. Accurate and temporally resolved fields of free-troposphere ozone are of major importance to quantify the intercontinental transport of pollution and the ozone radiative forcing. We consider a global chemical transport model (MOdèle de Chimie Atmosphérique à Grande Échelle, MOCAGE) in combination with a linear ozone chemistry scheme to examine the impact of assimilating observations from the Microwave Limb Sounder (MLS) and the Infrared Atmospheric Sounding Interferometer (IASI). The assimilation of the two instruments is performed by means of a variational algorithm (4D-VAR) and allows to constrain stratospheric and tropospheric ozone simultaneously. The analysis is first computed for the months of August and November 2008 and validated against ozonesonde measurements to verify the presence of observations and model biases. Furthermore, a longer analysis of 6 months (July–December 2008) showed that the combined assimilation of MLS and IASI is able to globally reduce the uncertainty (root mean square error, RMSE) of the modeled ozone columns from 30 to 15% in the upper troposphere/lower stratosphere (UTLS, 70–225 hPa). The assimilation of IASI tropospheric ozone observations (1000–225 hPa columns, TOC – tropospheric O3 column) decreases the RMSE of the model from 40 to 20% in the tropics (30° S–30° N), whereas it is not effective at higher latitudes. Results are confirmed by a comparison with additional ozone data sets like the Measurements of OZone and wAter vapour by aIrbus in-service airCraft (MOZAIC) data, the Ozone Monitoring Instrument (OMI) total ozone columns and several high-altitude surface measurements. Finally, the analysis is found to be insensitive to the assimilation parameters. We conclude that the combination of a simplified ozone chemistry scheme with frequent satellite observations is a valuable tool for the long-term analysis of stratospheric and free-tropospheric ozone.

scholarly journals The impact of multi-species surface chemical observations assimilation on the air quality forecasts in China

2018 ◽  
Author(s):  
Zhen Peng ◽  
Lili Lei ◽  
Zhiquan Liu ◽  
Jianning Sun ◽  
Aijun Ding ◽  
...  
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Initial Conditions ◽  
Yangtze River Delta ◽  
River Delta ◽  
Chemical Transport Model ◽  
The North ◽  
Surface Measurements ◽  
Haze Episode ◽  
The Impact

Abstract. An Ensemble Kalman Filter data assimilation (DA) system has been developed to improve air quality forecasts using surface measurements of PM10, PM2.5, SO2, NO2, O3 and CO together with an online regional chemical transport model, WRF-Chem (Weather Research and Forecasting with Chemistry). This DA system was applied to simultaneously adjust the chemical initial conditions (ICs) and emission inputs of the species affecting PM10, PM2.5, SO2, NO2, O3 and CO concentrations during an extreme haze episode that occurred in early October 2014 over the North China Plain. Numerical experimental results indicate that ICs play key roles in PM2.5, PM10 and CO forecasts during the severe haze episode. The 72-h verification forecasts with the optimized ICs and emissions performed very similarly to the verification forecasts with only optimized ICs and the prescribed emissions. For the first-day forecast, near perfect verification forecasts results were achieved. However, with longer range forecasts, the DA impacts decayed quickly. For the SO2 verification forecasts, it was efficient to improve the SO2 forecast via the joint adjustment of SO2 ICs and emissions. Large improvements were achieved for SO2 forecasts with both the optimized ICs and emissions for the whole 72-h forecast range. Similar improvements were achieved for SO2 forecasts with optimized ICs only for just the first 3 h, and then the impact of the ICs decayed quickly. For the NO2 verification forecasts, both forecasts performed much worse than the control run without DA. Plus, the 72-h O3 verification forecasts performed worse than the control run during the daytime, due to the worse performance of the NO2 forecasts, even though they performed better at night. However, relatively favorable NO2 and O3 forecast results were achieved for the Yangtze River delta and Pearl River delta regions.

scholarly journals Integration of remote sensing data and surface observations to estimate the impact of the Russian wildfires over Europe and Asia during August 2010

2011 ◽  
Vol 8 (12) ◽  
pp. 3771-3791 ◽  
Author(s):  
L. Mei ◽  
Y. Xue ◽  
G. de Leeuw ◽  
J. Guang ◽  
Y. Wang ◽  
...  
Keyword(s):  
Transport Model ◽  
Model Simulation ◽  
Arctic Region ◽  
The Arctic ◽  
Negative Consequences ◽  
Chemical Transport Model ◽  
Ozone Monitoring Instrument ◽  
Retrieval Models ◽  
Hysplit Model ◽  
The Impact

Abstract. A series of wildfires broke out in Western Russia starting in late July of 2010. Harmful particulates and gases released into the local Russian atmosphere have been reported, as have possible negative consequences for the global atmosphere. In this study, an extremely hazy area and its transport trajectory on Russian wildfires were analysed using aerosol optical depth (AOD) images retrieved via the synergy method from Moderate Resolution Imaging Spectroradiometer (MODIS) data. In addition, we used trace gases (NO2 and SO2) and CO2 products measured using Ozone Monitoring Instrument (OMI) data, vertical distribution of AOD data retrieved from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the mass trajectory analyses, synoptic maps from a HYSPLIT model simulation and ground-based data, including AERONET (both AOD and Ångström exponent) data and PM2.5. First, an Optimal Smoothing (OS) scheme was used to develop more precise and reliable AOD data based on multiple competing predictions made using several AOD retrieval models; then, integrated AOD and PM2.5 data were related using a chemical transport model (GEOS-Chem), and the integrated AOD and visibility data were related using the 6S radiative transfer code. The results show that the PM2.5 concentration is enhanced by a factor of 3–5 as determined from both satellite and in situ observations with peak daily mean concentrations of approximately 500 μg m3. Also, the visibility in many parts of Russia, for instance in Moscow, was less than 100 m; in some areas, the visibility was less than 50 m. Additionally, the possible impact on neighbouring countries due to long-transport was analysed for 31 July and 15 August 2010. A comparison of the satellite aerosol products and ground observations from the neighbouring countries suggests that wildfires in Western Russian had little impact on most european and asian countries, the exceptions being Finland, Estonia, Ukraine and Kyrgyzstan. However, a possible impact on the Arctic region was identified; such an effect would have a serious influence on the polar atmospheric enviroment, and on animals such as polar bears.

scholarly journals The impact of multi-species surface chemical observation assimilation on air quality forecasts in China

2018 ◽  
Vol 18 (23) ◽  
pp. 17387-17404 ◽  
Author(s):  
Zhen Peng ◽  
Lili Lei ◽  
Zhiquan Liu ◽  
Jianning Sun ◽  
Aijun Ding ◽  
...  
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Initial Conditions ◽  
Yangtze River Delta ◽  
River Delta ◽  
Chemical Transport Model ◽  
The North ◽  
Surface Measurements ◽  
Haze Episode ◽  
The Impact

Abstract. An ensemble Kalman filter data assimilation (DA) system has been developed to improve air quality forecasts using surface measurements of PM10, PM2.5, SO2, NO2, O3, and CO together with an online regional chemical transport model, WRF-Chem (Weather Research and Forecasting with Chemistry). This DA system was applied to simultaneously adjust the chemical initial conditions (ICs) and emission inputs of the species affecting PM10, PM2.5, SO2, NO2, O3, and CO concentrations during an extreme haze episode that occurred in early October 2014 over East Asia. Numerical experimental results indicate that ICs played key roles in PM2.5, PM10 and CO forecasts during the severe haze episode over the North China Plain. The 72 h verification forecasts with the optimized ICs and emissions performed very similarly to the verification forecasts with only optimized ICs and the prescribed emissions. For the first-day forecast, near-perfect verification forecasts results were achieved. However, with longer-range forecasts, the DA impacts decayed quickly. For the SO2 verification forecasts, it was efficient to improve the SO2 forecast via the joint adjustment of SO2 ICs and emissions. Large improvements were achieved for SO2 forecasts with both the optimized ICs and emissions for the whole 72 h forecast range. Similar improvements were achieved for SO2 forecasts with optimized ICs only for the first 3 h, and then the impact of the ICs decayed quickly. For the NO2 verification forecasts, both forecasts performed much worse than the control run without DA. Plus, the 72 h O3 verification forecasts performed worse than the control run during the daytime, due to the worse performance of the NO2 forecasts, even though they performed better at night. However, relatively favorable NO2 and O3 forecast results were achieved for the Yangtze River delta and Pearl River delta regions.

scholarly journals Tourism Impacts, Tourism-Phobia and Gentrification in Historic Centers: The Cases of Málaga (Spain) and Gdansk (Poland)

2021 ◽  
Vol 13 (1) ◽  
pp. 408
Author(s):  
Fernando Almeida-García ◽  
Rafael Cortés-Macías ◽  
Krzysztof Parzych
Keyword(s):  
Comparative Research ◽  
Control Measures ◽  
Tourism Impacts ◽  
Tourist Destinations ◽  
Tourist Flows ◽  
Tourist Accommodation ◽  
And Control ◽  
The Impact ◽  
Urban Characteristics

This study analyzes the role of residents in urban tourist destinations affected by the increase in tourist flows, which have generated various problems such as tourism, gentrification and the emergence of tourism as a threat to residents. The role of residents in tourist destinations has not been analyzed regularly during the development process of destinations. We study two cases of historic centers in European cities, with the aim of comparing tourism problems, which are common to most European urban destinations. This study was conducted by administering surveys amongst residents of these historic centers (378 in Málaga, Spain, and 380 in Gdansk, Poland). These cities show a similar demographic size and urban characteristics. This is the first comparative research on tourism-phobia and gentrification in destinations, a field of analysis that is still not studied much. We develop specific scales to measure gentrification and tourism-phobia; moreover, we study the impact of some tourist problems that affect residents (noise, dirt, occupation of public spaces, etc.), and we show the spatial distribution of tourism-phobia. The same analysis instruments are used for both cities. The results of this study show that the tourism-phobia situation is different in the analyzed destinations. It is more intense in the case of Málaga than in Gdansk. The two historic centers are especially affected by the processes of increased tourist flows and the growth of new forms of tourist accommodation. The research results show that the residents’ annoyance caused by tourism gentrification is more intense than tourism-phobia. Both case studies highlight the residents’ complaints regarding the inadequate management of problems by public stakeholders and control measures.

scholarly journals Classification of multiwavelength transients with Machine Learning

2020 ◽  
Author(s):  
K Sooknunan ◽  
M Lochner ◽  
Bruce A Bassett ◽  
H V Peiris ◽  
R Fender ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Sample ◽  
Light Curves ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Test Time ◽  
Test Accuracy ◽  
Training Set ◽  
The Impact

Abstract With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the eleven classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78%. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97%, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19%.

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

scholarly journals The Impact of COVID-19 on Pedestrian Flow Patterns in Urban POIs—An Example from Beijing

2021 ◽  
Vol 10 (7) ◽  
pp. 479
Author(s):  
Yihang Li ◽  
Liyan Xu
Keyword(s):  
Prevention And Control ◽  
Control Period ◽  
Flow Patterns ◽  
Control Measures ◽  
Pedestrian Flow ◽  
Prevention Measures ◽  
Government Control ◽  
Functional Categories ◽  
And Control ◽  
The Impact

The COVID-19 pandemic is a major challenge for society as a whole, and analyzing the impact of the spread of the epidemic and government control measures on the travel patterns of urban residents can provide powerful help for city managers to designate top-level epidemic prevention policies and specific epidemic prevention measures. This study investigates whether it is more appropriate to use groups of POIs with similar pedestrian flow patterns as the unit of study rather than functional categories of POIs. In this study, we analyzed the hour-by-hour pedestrian flow data of key locations in Beijing before, during, and after the strict epidemic prevention and control period, and we found that the pedestrian flow patterns differed greatly in different periods by using a composite clustering index; we interpreted the clustering results from two perspectives: groups of pedestrian flow patterns and functional categories. The results show that depending on the specific stage of epidemic prevention and control, the number of unique pedestrian flow patterns decreased from four before the epidemic to two during the strict control stage and then increased to six during the initial resumption of work. The restrictions on movement are correlated with most of the visitations, and the release of restrictions led to an increase in the variety of unique pedestrian flow patterns compared to that in the pre-restriction period, even though the overall number of visitations decreased, indicating that social restrictions led to differences in the flow patterns of POIs and increased social distance.

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