scholarly journals Retrospective analysis of 2015–2017 wintertime PM<sub>2.5</sub> in China: response to emission regulations and the role of meteorology

2019 ◽  
Vol 19 (11) ◽  
pp. 7409-7427 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Pusheng Zhao ◽  
Min Chen
Keyword(s):  
Data Assimilation ◽  
Control Strategies ◽  
Dominant Role ◽  
Fine Particulate Matter ◽  
Emission Control ◽  
River Delta ◽  
Control Measures ◽  
Central China ◽  
Assimilation Experiment

Abstract. To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.

scholarly journals Retrospective analysis of 2015–2017 winter-time PM<sub>2.5</sub> in China: response to emission regulations and the role of meteorology

2018 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Pusheng Zhao ◽  
Min Chen
Keyword(s):  
Control Experiment ◽  
Control Strategies ◽  
Emission Control ◽  
Reanalysis Data ◽  
River Delta ◽  
Central China ◽  
Pressure System ◽  
Pollution Dispersion ◽  
Assimilation Experiment ◽  
Winter Time

Abstract. To better characterize the anthropogenic emission relevant aerosol species, the GSI-WRF/Chem data assimilation system was updated from the GOCART aerosol scheme to MOSAIC-4BIN scheme. Three year (2015–2017) winter-time (January) surface PM2.5 observations from 1600&amp;plus; sites were assimilated hourly using the updated 3DVAR system in the assimilation experiment CONC_DA. Parallel control experiment that did not employ DA (NO_DA) was also performed. Both experiments were verified against the surface PM2.5 observations, MODIS 550-nm AOD and also 550-nm AOD at 9 AERONET sites. In the NO_DA experiment using 2010_MEIC emissions, modeled PM2.5 are severely overestimated in Sichuan Basin (SB), Central China (CC), YRD (Yangzi River Delta), and PRD (Pearl River Delta) which indicated the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations in Northeastern China (NEC) and Xin Jiang (XJ) were also observed. The assimilation experiments significantly reduced the high biases of surface PM2.5 in SB, CC, YRD, and PRD, and also low biases in NEC. However the improvement of the low biases in XJ is relatively small due to the large difference between the observations and the model background in the DA process, likely indicating that the emissions in the model are seriously underestimated in this region. Assimilating surface PM2.5 also significantly changed the column AOD and resulted in closer agreement with MODIS data and observations at AERONET sites. The observations and the reanalysis data from assimilation experiment were used to investigate the year-to-year changes. As the differences of the reanalysis data (CONC_DA) among years reflect combining effects of meteorology and emission and the differences of modeling result from control experiment (NO_DA, with same emissions) among years reflect the separate effect of meteorology, the important roles of emission and meteorology in driving the changes in the three years can be distinguished and analyzed quantitatively. The analysis indicated that meteorology played different roles in 2016 and 2017: the higher pressure system, lower temperature and higher PBLH in 2016 are favorable for pollution dispersion (compared with 2015) while the situation is almost the opposite in 2017 (compared with 2016) that leads to the increasing PM2.5 from 2016 to 2017 although emission control strategy were implemented in both years. There are still large uncertainties in this approach especially the inaccurate emission input in the model brings large biases in the analysis.

scholarly journals 2015 and 2016 winter-time air pollution in China: SO<sub>2</sub> emission changes derived from a WRF/Chem-EnKF coupled data assimilation system

2019 ◽  
Author(s):  
Dan Chen ◽  
Zhiquan Liu ◽  
Junmei Ban ◽  
Min Chen
Keyword(s):  
Data Assimilation ◽  
Emission Reduction ◽  
Emission Inventory ◽  
Southern China ◽  
Control Strategies ◽  
River Delta ◽  
Central China ◽  
Western China ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Ambient pollutants in China changes significantly in recent years due to strict control strategies implemented by the government. The control strategies also bring uncertainties to both the bottom-up emission inventory and the model-ready gridded emission inputs especially in winter season. In this study, we updated the WRF/Chem-EnKF Data Assimilation system to quantitatively estimate the gridded hourly SO2 emissions using hourly surface observations as constraints. Different from our previous study, in which meteorology and emission were both perturbed to obtain larger spread aiming to improve forecast skills; in this study, only emission was perturbed to ensure analyzed emission purely reflect necessary adjustments due to the emission uncertainties. In addition, direct emissions instead of emission scaling factors were used as analysis variable, which allowed for the detection of new emission sources. 2010 MEIC emission inventory (for January) was used as priori to generate 2015 and 2016 January analyzed emissions. The SO2 emission changing trends for northern, western and southern China from 2010 to 2015 and that from 2015 to 2016 (for the month of January) were investigated. The January 2010–2015 differences showed inhomogeneous change patterns in different regions: (1) significant emission reduction in southern China, (2) significant emission reduction in larger cities but widely increase in surrounding suburban and rural regions for northern China which may indicate the missing raw coal combustion for winter heating that not taken into account in the priori emission inventory; (3) significantly large emission increase in western China due to the energy expansion strategy. This not only reflected the changes during the five years, but also combined the uncertainties in the priori emissions. The January 2015–2016 differences showed widely emission reduction from 2015 to 2016, indicating the stricter control strategy fully executed nationwide. These changes were corresponded to facts in reality, indicating that the updated DA system was capable to detect the emission deficiencies and optimize the emission. By generating the hourly analyzed emissions, the diurnal pattern of emissions (in terms of hourly factors) were also obtained. Forecast experiments showed the improvements by using analyzed emissions were much larger in southern China than that in northern and western China. For Sichuan Basin, Central China, Yangzi River Delta, and Pearl River Delta, BIAS and RMSE decreased by 61.8 %–78.2 % and 27.9 %–52.2 %, respectively, and correlation coefficients increased by 12.5 %–47.1 %. However, the improvement in northern and western China were limited due to small spread. Another limitation of the study is that the analyzed emissions are still model dependent, as the ensembles are conducted through WRF/Chem model and thus the performances of ensembles are model dependent.

scholarly journals China’s emission control strategies have suppressed unfavorable influences of climate on wintertime PM2.5 concentrations in Beijing since 2002

2020 ◽  
Author(s):  
Meng Gao ◽  
Kaili Lin ◽  
Shiqing Zhang ◽  
Ken kin lam Yung
Keyword(s):  
Control Strategies ◽  
Emission Control ◽  
Field Measurements ◽  
Meteorological Conditions ◽  
Control Measures ◽  
Anthropogenic Emissions ◽  
Recent Climate ◽  
Decadal Variations ◽  
Haze Days ◽  
Emission Control Measures

&lt;p&gt;Severe wintertime PM2.5 pollution in Beijing has been receiving increasing worldwide attention, yet the decadal variations remain relatively unexplored. Combining field measurements and model simulations, we quantified the relative influences of anthropogenic emissions and meteorological conditions on PM2.5 concentrations in Beijing overwinters of 2002-2016. Between the winters of 2011 and 2016, stringent emission control measures resulted in a 21% decrease in mean mass concentrations of PM2.5 in Beijing, with 7 fewer haze days per winter on average. Given the overestimation of PM2.5 by model, the effectiveness of stringent emission control measures might have been slightly overstated. With fixed emissions, meteorological conditions over the study period would have led to an increase of haze in Beijing, but the strict emission control measures have suppressed the unfavorable influences of recent climate. The unfavorable meteorological conditions are attributed to the weakening of the East Asia Winter Monsoon associated particularly with an increase in pressure associated with the Aleutian low.&lt;/p&gt;

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.

scholarly journals Pre-symptomatic and asymptomatic transmission of COVID-19: Implications for control measures in Qatar

2021 ◽  
Vol 2021 (3) ◽  
Author(s):  
Abdelaziz M Tawengi ◽  
Samantha Johnston ◽  
Soha Shawqi Albayat ◽  
Devendra Bansal ◽  
Shazia Ahmed ◽  
...  
Keyword(s):  
Control Strategies ◽  
Small Population ◽  
Control Measures ◽  
Rapid Expansion ◽  
Young Population ◽  
Epidemiological Evidence ◽  
World Cup ◽  
Fifa World Cup ◽  
Entry Points

Public health control measures for communicable diseases are often based on the identification of symptomatic cases. However, emerging epidemiological evidence demonstrates the role of pre-symptomatic and asymptomatic transmissions of coronavirus disease 2019 (COVID-19). Understanding high-risk settings where transmissions can occur from infected individuals without symptoms has become critical for improving the response to the pandemic. In this review, we discussed the evidence on the transmission of severe acute respiratory syndrome coronavirus-2, its effect on control strategies, and lessons that can be applied in Qatar. Although Qatar has a small population, it has a distinct setting for COVID-19 control. It has a largely young population and is mostly composed of expatriates particularly from the Middle East and Asia that reside in Qatar for work. Further key considerations for Qatar and travel include population movement during extended religious holiday periods, screening and tracing of visitors and residents at entry points into the country, and expatriates living and working in high-density settings. We also consider how its international airport serves as a major transit destination for the region, as Qatar is expected to experience a rapid expansion of visitors while preparing to host the FIFA World Cup in 2022.

scholarly journals Analysis of meteorological conditions and prediction of epidemic trend of 2019-nCoV infection in 2020

2020 ◽  
Author(s):  
Jin Bu ◽  
Dong-Dong Peng ◽  
Hui Xiao ◽  
Qian Yue ◽  
Yan Han ◽  
...  
Keyword(s):  
Pearl River Delta ◽  
Prevention And Control ◽  
River Delta ◽  
Urban Agglomeration ◽  
Control Measures ◽  
Central China ◽  
Pearl River ◽  
High Latitudes ◽  
Prevention And Control Measures ◽  
And Control

AbstractObjectiveTo investigate the meteorological condition for incidence and spread of 2019-nCoV infection, to predict the epidemiology of the infectious disease, and to provide a scientific basis for prevention and control measures against the new disease.MethodsThe meteorological factors during the outbreak period of the novel coronavirus pneumonia in Wuhan in 2019 were collected and analyzed, and were confirmed with those of Severe Acute Respiratory Syndrome (SARS) in China in 2003. Data of patients infected with 2019-nCoV and SARS coronavirus were collected from WHO website and other public sources.ResultsThis study found that the suitable temperature range for 2019-nCoV survival is (13-24 °C), among which 19°C lasting about 60 days is conducive to the spread between the vector and humans; the humidity range is 50%-80%, of which about 75% humidity is conducive to the survival of the coronavirus; the suitable precipitation range is below 30 mm/month. Cold air and continuous low temperature over one week are helpful for the elimination of the virus. The prediction results show that with the approach of spring, the temperature in north China gradually rises, and the coronavirus spreads to middle and high latitudes along the temperature line of 13-19 °C. The population of new coronavirus infections is concentrated in Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Shanghai and other urban agglomerations. Starting from May 2020, the Beijing-Tianjin-Hebei urban agglomeration, the Central China Zhengzhou-Wuhan urban agglomeration, the eastern Jiangsu-Zhejiang-Shanghai urban agglomeration, and the southern Pearl River Delta urban agglomeration are all under a high temperature above 24 °C, which is not conducive to the survival and reproduction of coronaviruses, so the epidemic is expected to end.ConclusionsA wide range of continuous warm and dry weather is conducive to the survival of 2019-nCoV. The coming of spring, in addition to the original Wuhan-Zhengzhou urban agglomeration in central China, means that the prevention and control measures in big cities located in mid-latitude should be strengthened, especially the monitoring of transportation hubs. The Pearl River Delta urban agglomeration is a concentrated area of population in south China, with a faster temperature rise than those in mid-high latitudes, and thus the prevention in this area should be prioritized. From a global perspective, cities with a mean temperature below 24 °C are all high-risk cities for 2019-nCoV transmission before June.

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