scholarly journals Air Quality Change in Seoul, South Korea under COVID-19 Social Distancing: Focusing on PM2.5

2020 ◽  
Vol 17 (17) ◽  
pp. 6208 ◽  
Author(s):  
Beom-Soon Han ◽  
Kyeongjoo Park ◽  
Kyung-Hwan Kwak ◽  
Seung-Bu Park ◽  
Han-Gyul Jin ◽  
...  
Keyword(s):  
Air Quality ◽  
South Korea ◽  
Air Pollutants ◽  
Fine Particulate Matter ◽  
Pollutant Emissions ◽  
Social Distancing ◽  
Synoptic Conditions ◽  
Range Transport ◽  
Before And After ◽  
Pm2.5 Concentration

Seoul, the most populous city in South Korea, has been practicing social distancing to slow down the spread of coronavirus disease 2019 (COVID-19). Fine particulate matter (PM2.5) and other air pollutants measured in Seoul over the two 30 day periods before and after the start of social distancing are analyzed to assess the change in air quality during the period of social distancing. The 30 day mean PM2.5 concentration decreased by 10.4% in 2020, which is contrasted with an average increase of 23.7% over the corresponding periods in the previous 5 years. The PM2.5 concentration decrease was city-wide and more prominent during daytime than at nighttime. The concentrations of carbon monoxide (CO) and nitrogen dioxide (NO2) decreased by 16.9% and 16.4%, respectively. These results show that social distancing, a weaker forcing toward reduced human activity than a strict lockdown, can help lower pollutant emissions. At the same time, synoptic conditions and the decrease in aerosol optical depth over the regions to the west of Seoul support that the change in Seoul’s air quality during the COVID-19 social distancing can be interpreted as having been affected by reductions in the long-range transport of air pollutants as well as local emission reductions.

scholarly journals Environmental Effective Assessment of Control Measures Implemented by Clean Air Action Plan (2013–2017) in Beijing, China

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 189 ◽  
Author(s):  
Yifeng Xue ◽  
Shihao Zhang ◽  
Teng Nie ◽  
Xizi Cao ◽  
Aijun Shi
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Action Plan ◽  
Clean Energy ◽  
Control Measures ◽  
Pollutant Emissions ◽  
Total Reduction ◽  
Clean Air ◽  
Pm2.5 Concentration

The Beijing government initiated the Clean Air Action Plan (CAAP) in 2013. Through a series of actions to control air pollution, the emissions of major atmospheric pollutants are reduced to improve urban air quality. In order to evaluate the effectiveness of control measures taken to mitigate atmospheric pollution, we investigated and analyzed the implementation of the CAAP in Beijing from 2013 to 2017, estimating the corresponding reduction in emissions of major air pollutants. The contribution of different control measures to the improvement of air quality was quantified and the experiences of managing air pollution were summarized, which provided references for the continuous improvement of air quality in Beijing and the surrounding areas. The results showed that the emission of SO2, NOX, PM10, PM2.5, and VOCs from air pollution source have been decreased by 119,924, 116,091, 116,810, 46,652, and 97,267 tons after the implementation of the CAAP. The sum of these five air pollutants emissions have been reduced by 39% in 2017 compared with 2013, the largest decrease in SO2 emissions was 87%, which was related to the vigorous control on coal-fired combustion. The control measure with the greatest contribution to decreasing the ambient PM2.5 concentration was the clean energy transformation of coal-fired power plants, which contributed 27% of the total reduced concentration and 6.1 μg/m3 of the average PM2.5 concentration reduction in Beijing. Clean Residential coal use also significantly decreased the PM2.5 concentration by 5.4 μg/m3, which was 23% of the total reduction. In addition, the industrial restructuring and the management of automotive vehicle use and dust could also contribute to efficiently reducing the PM2.5 concentration by 4.0, 3.2, and 2.3 μg/m3, or 17%, 14%, and 10% of the total reduction, respectively. Due to the implementation of control measures of Clean Air Action Plan, the energy and industrial structure of Beijing have been adjusted and optimized, leading to the reduction of pollutant emissions, which is the secret of urban long-term air quality improvement.

scholarly journals Air Quality Variation in Wuhan, Daegu, and Tokyo during the Explosive Outbreak of COVID-19 and Its Health Effects

2020 ◽  
Vol 17 (11) ◽  
pp. 4119 ◽  
Author(s):  
Chang-Jin Ma ◽  
Gong-Unn Kang
Keyword(s):  
Air Quality ◽  
Allergic Airway Inflammation ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Self Reflection ◽  
Short Period ◽  
Before And After ◽  
The City ◽  
Quality Variation ◽  
Pm2.5 Concentration

This study was designed to assess the variation of the air quality actually measured from the air pollution monitoring stations (AQMS) in three cities (Wuhan, Daegu, and Tokyo), in Asian countries experiencing the explosive outbreak of COVID-19, in a short period of time. In addition, we made a new attempt to calculate the reduced DosePM2.5 (μg) at the bronchiolar (Br.) and alveolar-interstitial (AI) regions of the 10-year-old children after the city lockdown/self-reflection of each city. A comparison of the average PM2.5 of a month before and after the lockdown (Wuhan) and self-reflection (Daegu and Tokyo) clearly shows that the PM2.5 concentration was decreased by 29.9, 20.9, and 3.6% in Wuhan, Daegu and Tokyo, respectively. Wuhan, Daegu and Tokyo also recorded 53.2, 19.0, and 10.4% falls of NO2 concentration, respectively. Wuhan, which had the largest decrease of PM2.5 concentration due to COVID-19, also marked the largest reduced DosePM2.5 10-year-old children (μg) (3660 μg at Br. and 6222 μg at AI), followed by Daegu (445 μg at Br. and 1287 μg at AI), and Tokyo (18 μg at Br. and 52 μg at AI), over two months after the city lockdown/self-reflection. Our results suggest that the city lockdown/self-reflection had the effect of lowering the concentration of PM2.5, resulting in an extension of the period it took to the acute allergic airway inflammation (AAI) for the 10-year-old children.

scholarly journals Contributions of Trans-boundary Transport to the Summertime Air Quality in Beijing, China

2016 ◽  
Author(s):  
Jiarui Wu ◽  
Guohui Li ◽  
Junji Cao ◽  
Naifang Bei ◽  
Yichen Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Control Strategies ◽  
Dominant Role ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
Pollutant Emissions ◽  
Beijing China ◽  
Local Emissions ◽  
Factor Separation ◽  
Good Agreement

Abstract. In the present study, the WRF-CHEM model is used to evaluate the contributions of trans-boundary transport to the air quality in Beijing during a persistent air pollution episode from 5 to 14 July 2015 in Beijing-Tianjin-Hebei (BTH), China. Generally, the predicted temporal variations and spatial distributions of PM2.5 (fine particulate matter), O3 (ozone), and NO2 are in good agreement with observations in BTH. The WRF-CHEM model also reproduces reasonably well the temporal variations of aerosol species compared to measurements in Beijing. The factor separation approach is employed to evaluate the contributions of trans-boundary transport of emissions outside of Beijing to the PM2.5 and O3 levels in Beijing. On average, in the afternoon during the simulation episode, the pure local emissions contribute 22.4 % to the O3 level in Beijing, less than 36.6 % from pure emissions outside of Beijing. The O3 concentrations in Beijing are decreased by 5.1 % in the afternoon due to interactions of local emissions with those outside of Beijing. The pure emissions outside of Beijing play a dominant role in the PM2.5 level in Beijing, with a contribution of 61.5 %, much more than 13.7 % from pure Beijing local emissions. The emissions interactions enhance the PM2.5 concentrations in Beijing, with a contribution of 5.9 %. Therefore, the air quality in Beijing is primarily determined by the trans-boundary transport of emissions outside of Beijing during summertime, showing that the cooperation with neighboring provinces to mitigate pollutant emissions is a key for Beijing to improve air quality. Considering the uncertainties in the emission inventory and the meteorological field simulations, further studies need to be performed to improve the WRF-CHEM model simulations to reasonably evaluate trans-boundary transport contributions to the air quality in Beijing for supporting the design and implementation of emission control strategies.

Improving air quality forecasting with the assimilation of GOCI AOD retrievals

2020 ◽  
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Positive Impact ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Basis Set ◽  
Upstream Region ◽  
The Impact ◽  
Air Quality Forecasting

<p>The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).</p>

Study of Effects of Long-Range Transport of Air Pollutants on the Atmospheric Environment

2014 ◽  
Vol 955-959 ◽  
pp. 1341-1345 ◽  
Author(s):  
Xia Zhang ◽  
Liang Tian ◽  
Xian Sun ◽  
Chuang Ye Jiang
Keyword(s):  
Air Quality ◽  
Long Range ◽  
Air Pollutants ◽  
Concentration Field ◽  
Atmospheric Environment ◽  
Shanxi Province ◽  
Long Range Transport ◽  
Flux Method ◽  
Range Transport ◽  
Pollutants Transport

Based on meteorological field output by MM5 mesoscale meteorological model and concentration field output by CALPUFF air quality model, “flux method” was applied to study effects of long-range transport of air pollutants on the atmospheric environment, in which micro-element method was used to solve the process of air pollutants transport in long-range of three-dimensional space. This method was first applied in studying a construction project’s impact on air quality in Guanzhong region of Shanxi Province. The results shows that the deviation of flux method is less which the value is 16 percent, and among all year around, the pollutants transport the more flux to the ENE and WSW directions of the project compared to other directions. Additional, the flux of fall and winter is more than it of spring and summer, and the project has a more severe influence of atmospheric environment in Xi’an city than it of Weinan city.

scholarly journals Continuous mapping of fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia at daily 6×6 km<sup>2</sup> resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data

2021 ◽  
Author(s):  
Drew C. Pendergrass ◽  
Daniel J. Jacob ◽  
Shixian Zhai ◽  
Jhoon Kim ◽  
Ja-Ho Koo ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Random Forest ◽  
South Korea ◽  
East Asia ◽  
Fine Particulate Matter ◽  
Eastern China ◽  
Ambient Air ◽  
Population Exposure ◽  
Fine Particulate

Abstract. We use 2011–2019 aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) instrument over East Asia to infer 24-h daily surface fine particulate matter (PM2.5) concentrations at continuous 6x6 km2 resolution over eastern China, South Korea, and Japan. This is done with a random forest (RF) algorithm applied to the gap-filled GOCI AODs and other data and trained with PM2.5 observations from the three national networks. The predicted 24-h PM2.5 concentrations for sites entirely withheld from training in a ten-fold crossvalidation procedure correlate highly with network observations (R2 = 0.89) with single-value precision of 26–32 % depending on country. Prediction of annual mean values has R2 = 0.96 and single-value precision of 12 %. The RF algorithm is only moderately successful for diagnosing local exceedances of the National Ambient Air Quality Standard (NAAQS) because these exceedances are typically within the single-value precisions of the RF, and also because of RF smoothing of extreme PM2.5 concentrations. The area-weighted and population-weighted trends of RF PM2.5 concentrations for eastern China, South Korea, and Japan show steady 2015–2019 declines consistent with surface networks, but the surface networks in eastern China and South Korea underestimate population exposure. Further examination of RF PM2.5 fields for South Korea identifies hotspots where surface network sites were initially lacking and shows 2015–2019 PM2.5 decreases across the country except for flat concentrations in the Seoul metropolitan area. Inspection of monthly PM2.5 time series in Beijing, Seoul, and Tokyo shows that the RF algorithm successfully captures observed seasonal variations of PM2.5 even though AOD and PM2.5 often have opposite seasonalities. Application of the RF algorithm to urban pollution episodes in Seoul and Beijing demonstrates high skill in reproducing the observed day-to-day variations in air quality as well as spatial patterns on the 6 km scale. Comparison to a CMAQ simulation for the Korean peninsula demonstrates the value of the continuous RF PM2.5 fields for testing air quality models, including over North Korea where they offer a unique resource.

scholarly journals Tropical Cyclone as a Possible Remote Controller of Air Quality over South Korea through Poleward-Propagating Rossby Waves

2019 ◽  
Vol 58 (11) ◽  
pp. 2523-2530
Author(s):  
Doo-Sun R. Park ◽  
Chang-Hoi Ho ◽  
Dasol Kim ◽  
Nam-Young Kang ◽  
Yeojin Han ◽  
...  
Keyword(s):  
Air Quality ◽  
Tropical Cyclone ◽  
South Korea ◽  
Rossby Wave ◽  
Rossby Waves ◽  
North Pacific Ocean ◽  
Wave Train ◽  
Pollutant Emissions ◽  
Rossby Wave Train ◽  
The Tropics

AbstractAir quality depends as much on large-scale tropospheric circulation as on the amount of pollutant emissions. Many studies have found a relationship between air quality and midlatitude synoptic weather systems. A stable low-level troposphere and airflow from polluted areas are conditions that favor air pollution in a region. However, few studies have focused on the possible remote effect of tropical cyclone (TC) activity in the tropics on air quality in the midlatitude East Asian countries. Here, we found that TCs in the South China Sea (SCS) can increase the concentration of particulate matter with aerodynamic diameters less than 10 μm (PM10) over South Korea through poleward-propagating Rossby waves. According to our analyses, intense divergence due to a TC causes a barotropic Rossby wave train from the SCS to the North Pacific Ocean. Anomalous highs over the Korean Peninsula (part of the Rossby wave train) result in stable air conditions and cause polluted air inflow to increase the PM10 concentration up to 65 μg m−3. Our finding suggests that TC activity in the tropics should be considered for more accurate forecasts of air quality in South Korea.

scholarly journals Particulate matter concentrations during winter seasons of 2016-2020 in Ulaanbaatar, Mongolia

2021 ◽  
pp. 30-39
Author(s):  
Ganzorig Byambajav ◽  
Bayarmaa Batbaatar ◽  
Ariundelger Ariunsaikhan ◽  
Sonomdagva Chonokhuu
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Single Point ◽  
Fine Particulate Matter ◽  
Measurement Data ◽  
Central Area ◽  
National Standards ◽  
Fine Particulate ◽  
The Government ◽  
Pm2.5 Concentration

In this study, we have focused on the outdoor concentration of fine particulate matter (PM2.5) during the coldest months (November-February) of 2016-2019 and January-February of 2020 and illustrated the daily, monthly and quarterly averages according to the single-point measurement data collected by the PM2.5 sensor at an air quality monitoring station located in a central area of Ulaanbaatar. The study also analyzes monthly high, low, average and median points of PM2.5 concentrations in the area that was selected. The PM2.5 sensor collects its data at an interval of every ten seconds, registers 8500 data in one day and presents the concentration of fine particulate matter in micrograms per cubic meter (μg/m3). On the basis of data collection and analysis, from November through February of 2019-2020, average PM2.5 concentration dropped noticeably by 44 per cent compared to the previous years. The Government of Mongolia took immediate action to combat air pollution of Ulaanbaatar city in May 2019 by banning the burning of raw coal in the ger districts, which account for 70 per cent of the city’s emissions, and introduced coal briquette as the only type of fuel that was allowed to be burned in metal stoves as a primary source of heating and cooking. Our study reveals that the latest government regulation had a considerable impact on air quality during winter 2019-2020 and helped in the sudden decline of the most dangerous pollutant PM2.5 concentration very close to national standards (50 µg/m3 24-hour mean) within 6 months since the enforcement of the new regulation.

scholarly journals Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model

2020 ◽  
Vol 17 (24) ◽  
pp. 9471
Author(s):  
Dongsheng Wang ◽  
Hong-Wei Wang ◽  
Chao Li ◽  
Kai-Fa Lu ◽  
Zhong-Ren Peng ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Traffic Management ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
Learning Ability ◽  
Observation Data ◽  
Deterministic Models ◽  
Co Concentrations ◽  
Air Quality Forecasting

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident’s outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.

Satellite-based observations of ground-level fine particulate matter and comparison to a regional air quality model

2020 ◽  
Author(s):  
Jana Handschuh ◽  
Frank Baier ◽  
Thilo Erbertseder ◽  
Martijn Schaap
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutants ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
In Situ Measurements ◽  
Satellite Observations ◽  
Optical Parameters ◽  
Fine Particulate

&lt;p&gt;Particulate matter and other air pollutants have become an increasing burden on the environment and human health. Especially in metropolitan and high-traffic areas, air quality is often remarkably reduced. For a better understanding of the air quality in specific areas, which is of great environment-political interest, data with high resolution in space and time is required. The combination of satellite observations and chemistry-transport-modelling has proven to give a good database for assessments and analyses of air pollution. In contrast to sample in-situ measurements, satellite observations provide area-wide coverage &amp;#8203;&amp;#8203;of measurements and thus the possibility for an almost gapless mapping of actual air pollutants. For a high temporal resolution, chemistry-transport-models are needed, which calculate concentrations of specific pollutants in continuous time steps. Satellite observations can thus be used to improve model performances.&lt;/p&gt;&lt;p&gt;There are no direct satellite-measurements of fine particulate matter (PM2.5) but ground-level concentrations of PM2.5 can be derived from optical parameters such as aerosol optical depth (AOD). A wide range of methods for the determination of PM2.5 concentrations from AOD measurements has been developed so far, but it is still a big challenge. In this study a semi-empirical approach based on the physical relationships between meteorological and optical parameters was applied to determine a first-guess of ground-level PM2.5 concentrations for the year 2018 and the larger Germany region. Therefor AOD observations of MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the NASA Aqua satellite were used in a spatial resolution of 3km. First results showed an overestimation of ground-level aerosols and quiet low correlations with in-situ station measurements from the European Environmental Agency (EEA). To improve the results, correction factors were calculated using the coefficients of linear regression between satellite-based and in-situ measured particulate matter concentrations. Spatial and seasonal dependencies were taken into account with it. Correlations between satellite and in-situ measurements could be improved applying this method.&lt;/p&gt;&lt;p&gt;The MODIS 3km AOD product was found to be a good base for area-wide calculations of ground-level PM2.5 concentrations. First comparisons to the calculated PM2.5 concentrations from chemistry-transport-model POLYPHEMUS/DLR showed significant differences though. Satellite observations will now be used to improve the general model performance, first by helping to find and understand regional and temporal dependencies in the differences. As part of the German project S-VELD funded by the Federal Ministry of Transport and Digital Infrastructure BMVI, it will help for example to adjust the derivation of particle emissions within the model.&lt;/p&gt;

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