Assessment of the meteorological impact on improved PM 2.5 air quality over North China during 2016−2019 based on a regional joint atmospheric composition reanalysis dataset

Meteorological impact on winter PM 2.5 pollution in Delhi: Present and future projection under a warming climate

Geophysical Research Letters ◽

10.1029/2021gl093722 ◽

2021 ◽

Author(s):

Jiandong Li ◽

Xin Hao ◽

Hong Liao ◽

Jianlin Hu ◽

Haishan Chen

Keyword(s):

Future Projection ◽

Pm 2.5 ◽

Warming Climate ◽

Meteorological Impact

Clean air in the Anthropocene

Faraday Discussions ◽

10.1039/c7fd90032e ◽

2017 ◽

Vol 200 ◽

pp. 693-703 ◽

Cited By ~ 18

Author(s):

Jos Lelieveld

Keyword(s):

Air Pollution ◽

Sustainable Development ◽

Air Quality ◽

Human Health ◽

Atmospheric Chemistry ◽

Clean Energy ◽

Atmospheric Composition ◽

Anthropogenic Sources ◽

Years Of Life Lost ◽

Clean Air

In atmospheric chemistry, interactions between air pollution, the biosphere and human health, often through reaction mixtures from both natural and anthropogenic sources, are of growing interest. Massive pollution emissions in the Anthropocene have transformed atmospheric composition to the extent that biogeochemical cycles, air quality and climate have changed globally and partly profoundly. It is estimated that mortality attributable to outdoor air pollution amounts to 4.33 million individuals per year, associated with 123 million years of life lost. Worldwide, air pollution is the major environmental risk factor to human health, and strict air quality standards have the potential to strongly reduce morbidity and mortality. Preserving clean air should be considered a human right, and is fundamental to many sustainable development goals of the United Nations, such as good health, climate action, sustainable cities, clean energy, and protecting life on land and in the water. It would be appropriate to adopt “clean air” as a sustainable development goal.

Air Quality and Climate Change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III), Part I: overview and model evaluation

10.5194/acp-2017-731 ◽

2017 ◽

Cited By ~ 1

Author(s):

Meng Gao ◽

Zhiwei Han ◽

Zirui Liu ◽

Meng Li ◽

Jinyuan Xin ◽

...

Keyword(s):

Air Quality ◽

Model Evaluation ◽

Air Pollutants ◽

North China ◽

Correlation Coefficients ◽

Phase Iii ◽

Comparison Study ◽

Model Ensemble ◽

Near Surface ◽

Ensemble Mean

Abstract. Topic 3 of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase III examines how online coupled air quality models perform in simulating high aerosol pollution in the North China Plain region during wintertime haze events and evaluates the importance of aerosol radiative and microphysical feedbacks. A comprehensive overview of the MICS-ASIA III Topic 3 study design, including descriptions of participating models and model inputs, the experimental designs, and results of model evaluation, are presented. Two winter months (January 2010 and January 2013) were selected as study periods, when severe haze occurred in North China. Simulations were designed to evaluate radiative and microphysical feedbacks, together and separately, relative to simulations without feedbacks. Six modeling groups from China, Korea and the United States submitted results from seven applications of online coupled chemistry-meteorology models. Results are compared to meteorology and air quality measurements, including the Campaign on Atmospheric Aerosol Research Network of China (CARE-China) network, and the Acid Deposition Monitoring Network in East Asia (EANET). The analysis focuses on model evaluations and aerosol effects on meteorology and air quality, and potentially other interesting topics, such as the impacts of model resolutions on aerosol-radiation-weather interactions. The model evaluations for January 2010 show that current online-coupled meteorology-chemistry model can generally well reproduced meteorological features and variations of major air pollutants, including aerosol concentrations. The correlation coefficients between multi-model ensemble mean and observed near-surface temperature, water vapor mixing ratio and wind speeds can reach as high as 0.99, 0.99 and 0.98. The correlation coefficients between multi-model ensemble mean and the CARE-China observed near-surface air pollutants range from 0.51 to 0.94 (0.51 for ozone and 0.94 for PM2.5). However, large discrepancies exist between simulated aerosol chemical compositions from different models, which is due to different parameterizations of chemical reactions. The coefficient of variation (standard deviation divided by average) can reach above 1.3 for sulfate in Beijing, and above 1.6 for nitrate and organic aerosol in coastal regions, indicating these compositions are less consistent from different models. During clean periods, simulated Aerosol Optical Depths (AOD) from different models are consistent, but peak values differ during severe haze event, which can be explained by the differences in simulated inorganic aerosol concentrations and the hygroscopic growth efficiency (affected by varied RH). These results provide some brief senses of how current online-coupled meteorology-chemistry models reproduce severe haze events, and some directions for future model improvements.

Assessment of Meteorological Impact and Emergency Plan for a Heavy Haze Pollution Episode in a Core City of the North China Plain

Aerosol and Air Quality Research ◽

10.4209/aaqr.2019.08.0392 ◽

2020 ◽

Vol 20 (1) ◽

pp. 26-42 ◽

Cited By ~ 2

Author(s):

Simeng Ma ◽

Zhimei Xiao ◽

Yufen Zhang ◽

Litao Wang ◽

Min Shao

Keyword(s):

North China Plain ◽

North China ◽

Emergency Plan ◽

The North ◽

The North China Plain ◽

Pollution Episode ◽

Haze Pollution ◽

Meteorological Impact

Near real-time air quality forecasts using the NASA GEOS model

10.5194/egusphere-egu21-13587 ◽

2021 ◽

Author(s):

K. Emma Knowland ◽

Christoph Keller ◽

Krzysztof Wargan ◽

Brad Weir ◽

Pamela Wales ◽

...

Keyword(s):

Machine Learning ◽

Air Quality ◽

Real Time ◽

Weather Forecasting ◽

Atmospheric Composition ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Wide Range ◽

Reactive Trace Gases ◽

Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

A new approach to estimate pollutant emissions based on trajectory modelling and its application in the North China Plain

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-31137-2011 ◽

2011 ◽

Vol 11 (11) ◽

pp. 31137-31158 ◽

Cited By ~ 1

Author(s):

W. Y. Xu ◽

C. S. Zhao ◽

P. F. Liu ◽

L. Ran ◽

N. Ma ◽

...

Keyword(s):

Air Quality ◽

North China Plain ◽

North China ◽

Pollutant Emissions ◽

New Approach ◽

The North ◽

The North China Plain ◽

Source Contributions ◽

Co Emission

Abstract. Emission information is crucial for air quality modelling and air quality management. In this study, a new approach based on the understanding of the relationship between emissions and measured pollutant concentrations has been proposed to estimate pollutant emissions and source contributions. The retrieval can be made with single point in-situ measurements combined with backward trajectory analyses. The method takes into consideration the effect of meteorology on pollutant transport when evaluating contributions and is independent of energy statistics, therefore can provide frequent updates on emission information. The spatial coverage can be further improved by using measurements from several sites and combining the derived emission fields. The method was applied to yield the source distributions of black carbon (BC) and CO in the North China Plain (NCP) using in-situ measurements from the HaChi (Haze in China) Campaign and to evaluate contributions from specific areas to local concentrations at the measurement site. Results show that this method can yield a reasonable emission field for the NCP and can directly quantify areal source contributions. Major BC and CO emission source regions are Beijing, the western part of Tianjin and Langfang, Hebei, with Tangshan being an additional important CO emission source area. The source contribution assessment suggests that, aside from local emissions in Wuqing, Tianjin and Hebei S, SW (d < 100 km) are the greatest contributors to measured local concentrations, while emissions from Beijing contribute little during summertime.

Insights into particulate matter pollution in the North China Plain during wintertime: Local contribution or regional transport?

10.5194/acp-2020-597 ◽

2020 ◽

Author(s):

Jiarui Wu ◽

Naifang Bei ◽

Yuan Wang ◽

Xia Li ◽

Suixin Liu ◽

...

Keyword(s):

Air Quality ◽

Source Apportionment ◽

North China Plain ◽

North China ◽

Regional Transport ◽

The North ◽

The North China Plain ◽

Haze Event ◽

Non Local ◽

Local Emissions

Abstract. Accurate identification and quantitative source apportionment of fine particulate matters (PM2.5) provide an important prerequisite for design and implementation of emission control strategies to reduce PM pollution. Therefore, a source-oriented version of the WRF-Chem model is developed in the study to make source apportionment of PM2.5 in the North China Plain (NCP). A persistent and heavy haze event occurred in the NCP from 05 December 2015 to 04 January 2016 is simulated using the model as a case study to quantify PM2.5 contributions of local emissions and regional transport. Results show that local and non-local emissions contribute 36.3 % and 63.7 % of the PM2.5 mass in Beijing during the haze event on average. When Beijing's air quality is excellent or good in terms of hourly PM2.5 concentrations, local emissions dominate the PM2.5 mass with contributions exceeding 50 %. However, when the air quality is severely polluted, the PM2.5 contribution of non-local emissions is around 75 %. The non-local emissions also dominate the Tianjin's air quality, with average PM2.5 contributions exceeding 70 %. The PM2.5 level in Hebei and Shandong is generally controlled by local emissions, but in Henan, local and non-local emissions play an almost equivalent role in the PM2.5 level, except when the air quality is severely polluted, with non-local PM2.5 contributions of over 60 %. Additionally, the primary aerosol species are generally dominated by local emissions with the average contribution exceeding 50%. However, the source apportionment of secondary aerosols shows more evident regional characteristics. Therefore, except cooperation with neighboring provinces to carry out strict emission mitigation measures, reducing primary aerosols constitutes the priority to alleviate PM pollution in the NCP, especially in Beijing and Tianjin.

Numerical Prognosis of Dispersion and Transport of Pollutants in Romania, Based on Emissions of Pollutants

Present Environment and Sustainable Development ◽

10.2478/pesd-2019-0015 ◽

2019 ◽

Vol 13 (1) ◽

pp. 191-200

Author(s):

Bogdan Alexandru Maco ◽

Nicoleta Ionac ◽

George Tudorache

Keyword(s):

Air Quality ◽

National Level ◽

Main Tool ◽

Atmospheric Composition ◽

Numerical Dispersion ◽

Air Quality Model ◽

Quality Model ◽

Gravimetric Data ◽

Environment Agency ◽

Physical And Chemical

Abstract Air pollution is one of the major problems of mankind, transport of pollutants extending far beyond the borders of the countries where they were produced, causing unpredictable, direct and indirect changes of the environment. The main tool for the study of this phenomenon consists of mathematical modeling of complex physical and chemical phenomena involved. In practice, air emissions are estimated on basis of measurements taken from selected sources being representative of the major categories and types. At national level, the Air Quality Evaluation Center (CECA) provides regular reports to the European Environment Agency (EEA) or the European Commission as requirements of Romania’s lawful duties in air quality domain. The registry of emissions TNO/ MACC (Netherlands Organisation for Applied Scientific Research/ Monitoring Atmospheric Composition and Climate) contains emissions inventories which have been homogenized and checked in advance and obtained from emissions officially reported at sectoral level for each country. In this study, for the analysis of the weather numerical dispersion and transport of pollutants, it has been used the numerical air quality model WRF-CHEM version 3.5, centered over Romania, at the spatial resolution of 10 km, using as input data the TNO emission database for 2009. By interpolating values from the regular grid of the TNO database with the WRF-CHEM model 3.5 grid, monthly average values were obtained for each day of the week, for any parameter considered. Preliminary results obtained for different pollutants (for example: PM10, O3) confirm the need to validate these results by implementing and integrating air quality forecasting model by assimilating different types of measurements (data model, gravimetric data observations, etc.).

Measurement report: Evaluation of sources and mixing state of black carbon aerosol under the background of emission reduction in the North China Plain: implications for radiative effect

10.5194/acp-2020-570 ◽

2020 ◽

Author(s):

Qiyuan Wang ◽

Li Li ◽

Jiamao Zhou ◽

Jianhuai Ye ◽

Wenting Dai ◽

...

Keyword(s):

Air Quality ◽

Solid Fuel ◽

Emission Reduction ◽

North China Plain ◽

North China ◽

Fossil Fuel ◽

Radiative Effect ◽

The North ◽

The North China Plain ◽

Fuel Source

Abstract. Accurate understanding of sources and mixing state of black carbon (BC) aerosol is essential for assessing its impacts on air quality and climatic effect. Here, a winter campaign (December 2017–January 2018) was conducted in the North China Plain (NCP) to evaluate the sources, coating composition, and radiative effect of BC under the background of emission reduction since 2013. Results show that liquid fossil fuel source (i.e., traffic emission) and solid fuel source (i.e., biomass and coal burning) contributed 69 % and 31 % to the total BC mass, respectively, using a multiwavelength optical approach combined with the source-based aerosol absorption Ångström exponent values. The air quality model indicates that local emission was the dominant contributor to BC at the measurement site on average, however, emissions in the NCP exerted a critical role for high BC episode. Six classes of BC-containing particles were identified, including (1) BC coated by organic carbon and sulphate (52 % of total BC-containing particles), (2) BC coated by Na and K (24 %), (3) BC coated by K, sulphate, and nitrate (17 %), (4) BC associated with biomass burning (6 %), (5) Pure-BC (1 %), and (6) others (1 %). Different BC sources had distinct impacts on those BC-containing particles. A radiative transfer model estimated that the amount of BC detected can produce an atmospheric forcing of +18.0 W m−2 and a heating rate of 0.5 K day−1. Results presented herein highlight that further reduction of solid fuel combustion-related BC may be a more effective way to mitigate regional warming in the NCP, although larger BC contribution was from liquid fossil fuel source.

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

Atmosphere ◽

10.3390/atmos10090517 ◽

2019 ◽

Vol 10 (9) ◽

pp. 517 ◽

Cited By ~ 4

Author(s):

Prakhar Misra ◽

Ryoichi Imasu ◽

Wataru Takeuchi

Keyword(s):

Remote Sensing ◽

Land Use ◽

Developing Countries ◽

Air Quality ◽

Bayesian Approach ◽

Urban Growth ◽

Chemical Transport ◽

Pm 2.5 ◽

Brick Kilns ◽

Indian Cities

Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.