Application of ARPS–CMAQ modeling system for urban air pollutant emission abatementA paper submitted to the Journal of Environmental Engineering and Science.

2010 ◽  
Vol 37 (2) ◽  
pp. 323-334
Author(s):  
X. Y. Zhao ◽  
S. Y. Cheng ◽  
J. B. Li ◽  
H. Y. Wang ◽  
X. R. Guo
Keyword(s):  
Air Quality ◽  
Northern China ◽  
Air Pollutant ◽  
Decision Makers ◽  
Pollutant Emission ◽  
Advanced Regional Prediction System ◽  
Multi Scale ◽  
Regional Prediction ◽  
Quality Guideline ◽  
Quality Objective

A coupled advanced regional prediction system – community multi-scale air quality (ARPS–CMAQ) modeling system was applied to develop an abatement strategy for air pollutant emission in the Handan region of the northern China. The system was evaluated by comparing the simulated concentrations of particulate matter less than 10 µm (PM10) with the observed results in the study area during the four representative months in 2005. A process of planning emission abatement was applied by gradually reducing PM10 emissions from the original GIS-based emission inventory until a modeling emission scenario was obtained under which the simulated PM10 concentrations could satisfy the desired air quality objective. The air quality objective was represented by an air quality guideline satisfaction ratio of 80% to reach a daily PM10 concentration of 150 µg/m3 after the year 2010. The modeling system and results could provide sound basis for decision makers to develop an effective air quality management strategy.

scholarly journals Enhanced atmospheric oxidizing capacity in simulating air quality with updated emission inventories for power plants especially for haze periods over East China

2017 ◽  
Author(s):  
Lei Zhang ◽  
Tianliang Zhao ◽  
Sunling Gong ◽  
Shaofei Kong ◽  
Lili Tang ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Emission Inventory ◽  
Environmental Changes ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Atmospheric Environment ◽  
Pollutant Emission ◽  
East China ◽  
Emission Inventories

Abstract. Air pollutant emissions play a determinant role in deteriorating air quality. However, an uncertainty in emission inventories is still the key problem for modeling air pollution. In this study, an updated emission inventory of coal-fired power plants (UEIPP) based on online monitoring data in Jiangsu province of East China for the year of 2012 was implemented in the widely used Multi-resolution Emission Inventory for China (MEIC). By employing the Weather Research and Forecasting Model with Chemistry (WRF-Chem), two simulations were executed to assess the atmospheric environmental change by using the original MEIC emission inventory and the MEIC inventory with the UEIPP. A synthetic analysis shows that (1) compared to the power emissions of MEIC, PM2.5, PM10, SO2 and NOx were lower, and CO, black carbon (BC), organic carbon (OC) and NMVOCs were higher in the UEIPP, reflecting a large discrepancy in the power emissions over East China; (2) In accordance with the changes of UEIPP, the modeled concentrations were reduced for SO2 and NO2, and increased for most areas of primary OC, BC and CO, whose concentrations in atmosphere are highly dependent on emission changes. (3) Interestingly, when the UEIPP was used, the atmospheric oxidizing capacity significantly reinforced, reflecting by increased oxidizing agents, e.g. O3 and OH, thus directly strengthened the chemical production from SO2 and NOx to sulfate and nitrate, which offset the reduction of primary PM2.5 emissions especially in the haze days. This study indicated the importance of updating air pollutant emission inventories in simulating the complex atmospheric environment changes with the implications on air quality and environmental changes.

scholarly journals Sectorial and regional uncertainty analysis of the contribution of anthropogenic emissions to regional and global PM<sub>2.5</sub> health impacts

2017 ◽  
Author(s):  
Monica Crippa ◽  
Greet Janssens-Maenhout ◽  
Diego Guizzardi ◽  
Rita Van Dingenen ◽  
Frank Dentener
Keyword(s):  
Power Generation ◽  
Air Quality ◽  
Human Health ◽  
Emission Inventory ◽  
Air Pollutant ◽  
Health Impacts ◽  
Pollutant Emission ◽  
Emission Inventories ◽  
Anthropogenic Emission ◽  
Relative Contribution

Abstract. In this work we couple the HTAPv2.2 global air pollutant emission inventory with the global source receptor model TM5-FASST to evaluate the relative contribution of the major anthropogenic emission sources (power generation, industry, ground transport, residential, agriculture and international shipping) to air quality and human health in 2010. We focus on particulate matter (PM) concentrations because of the relative importance of PM2.5 emissions in populated areas and the proven cumulative negative effects on human health. We estimate that in 2010 regional annual averaged anthropogenic PM2.5 concentrations varied between ca. 1 and 40 μg/m3 depending on the region, with the highest concentrations observed in China and India, and lower concentrations in Europe and North America. The relative contribution of anthropogenic emission source sectors to PM2.5 concentrations varies between the regions. European PM pollution is mainly influenced by the agricultural and residential sectors, while the major contributing sectors to PM pollution in Asia and the emerging economies are the power generation, industrial and residential sectors. We also evaluate the emission sectors and emission regions in which pollution reduction measures would lead to the largest improvement on the overall air quality. We show that in order to improve air quality, regional policies should be implemented (e.g. in Europe) due to the transboundary features of PM pollution. In addition, we investigate emission inventory uncertainties and their propagation to PM2.5 concentrations, in order to identify the most effective strategies to be implemented at sector and regional level to improve emission inventories knowledge and air quality. We show that the uncertainty of PM concentrations depends not only on the uncertainty of local emission inventories but also on that of the surrounding regions. Finally, we propagate emission inventories uncertainty to PM concentrations and health impacts.

Simulation of Air Quality and Pollution Control Countermeasures of Chengdu Urban Agglomeration

2012 ◽  
Vol 610-613 ◽  
pp. 1387-1397 ◽  
Author(s):  
Wen Yong Wang ◽  
Nan Chen ◽  
Xiao Juan Ma
Keyword(s):  
Air Quality ◽  
Sulfur Dioxide ◽  
Pollution Sources ◽  
Air Pollutant ◽  
Urban Agglomeration ◽  
Pollutant Concentration ◽  
Pollutant Emission ◽  
Air Quality Model ◽  
Quality Model ◽  
Allowable Limit

The CMAQ model (Community Multiscale Air Quality model) was used to stimulate the atmospheric environmental quality of Chengdu urban agglomeration. The result shows that air pollutant concentration in some zones of the urban agglomeration is higher than the allowable limit of the national grade II standard. Fortunately, such zones only cover a small area. Zones where the average daily and annual PM10 concentration is higher than the allowable limit only account for 4% of the total area of Chengdu urban agglomeration. Less than 1% of the total area has the concentration of other pollutants higher than the limit. Zones with pollutant concentration higher than the limit are mainly distributed in Chengdu City, Mianyang City, and Meishan City. Pollutants emitted from the cities of Chengdu urban agglomeration shift on to and interact with each other. Therefore, the air pollutant concentration of one city is partially attributable to pollutants emitted from its own pollution sources and a part of or even most of it results from pollutants from other cities. For example, regarding PM10 in air of Deyang City, only 12% comes from its own pollution sources, and 55% comes from pollution sources of Chengdu, and the rest 29% comes from pollution sources of Mianyang. Regarding Sulfur dioxide in air of Chengdu, 59% comes from local pollution sources of Chengdu and 23% comes from pollution sources of Deyang. Other pollutants are also subject to such a rule. As in the urban agglomeration, there are zones where pollutant concentration is higher than the allowable limit, the existing pollution sources must be further controlled by setting reduction target according to the total capacity. The pollutant emission should be reduced by means of eliminating backward productivity, adjusting structure and layout of industries, and controlling pollution sources in depth to effectively improve the regional environmental air quality. At the same time, as pollutants emitted from the cities interact with each other, the 5 cities must sign a joint prevention and control agreement to collaborate in control of sulfur dioxide, nitrogen oxides, smoke and dust, and organic pollutants.

scholarly journals Does the “Blue Sky Defense War Policy” Paint the Sky Blue?—A Case Study of Beijing–Tianjin–Hebei Region, China

2021 ◽  
Vol 18 (23) ◽  
pp. 12397
Author(s):  
Xuan Yang ◽  
Yue Wang ◽  
Di Chen ◽  
Xue Tan ◽  
Xue Tian ◽  
...  
Keyword(s):  
Air Quality ◽  
Environmental Protection ◽  
Emission Reduction ◽  
Average Concentration ◽  
Air Pollutant ◽  
Pollutant Emission ◽  
Concentration Data ◽  
The North ◽  
The Difference ◽  
Bth Region

Improving air quality is an urgent task for the Beijing–Tianjin–Hebei (BTH) region in China. In 2018, utilizing 365 days’ daily concentration data of six air pollutants (including PM2.5, PM10, SO2, NO2, CO and O3) at 947 air quality grid monitoring points of 13 cities in the BTH region and controlling the meteorological factors, this paper takes the implementation of the Blue Sky Defense War (BSDW) policy as a quasi-natural experiment to examine the emission reduction effect of the policy in the BTH region by applying the difference-in-difference method. Results show that the policy leads to the significant reduction of the daily average concentration of PM2.5, PM10, SO2, O3 by −1.951 μg/m3, −3.872 μg/m3, −1.902 μg/m3, −7.882 μg/m3 and CO by −0.014 mg/m3, respectively. The results of the robustness test support the aforementioned conclusions. However, this paper finds that the concentration of NO2 increases significantly (1.865 μg/m3). In winter heating seasons, the concentration of SO2, CO and O3 decrease but PM2.5, PM10 and NO2 increase significantly. Besides, resource intensive cities, non-key environmental protection cities and cities in the north of the region have great potential for air pollutant emission reduction. Finally, policy suggestions are recommended; these include setting specific goals at the city level, incorporating more cities into the list of key environmental protection cities, refining the concrete indicators of domestic solid fuel, and encouraging and enforcing clean heating diffusion.

scholarly journals HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module

2020 ◽  
Vol 13 (3) ◽  
pp. 873-903
Author(s):  
Marc Guevara ◽  
Carles Tena ◽  
Manuel Porquet ◽  
Oriol Jorba ◽  
Carlos Pérez García-Pando
Keyword(s):  
Atmospheric Chemistry ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Companion Paper ◽  
Point Sources ◽  
Air Pollutant ◽  
Pollutant Emission ◽  
Emission Model ◽  
Bottom Up ◽  
Multi Scale

Abstract. We describe the bottom–up module of the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), a Python-based and multi-scale modelling tool intended for the processing and computation of atmospheric emissions for air quality modelling. HERMESv3 is composed of two separate modules: the global_regional module and the bottom_up module. In a companion paper (Part 1, Guevara et al., 2019a) we presented the global_regional module. The bottom_up module described in this contribution is an emission model that estimates anthropogenic emissions at high spatial- (e.g. road link level,) and temporal- (hourly) resolution using state-of-the-art calculation methods that combine local activity and emission factors along with meteorological data. The model computes bottom–up emissions from point sources, road transport, residential and commercial combustion, other mobile sources, and agricultural activities. The computed pollutants include the main criteria pollutants (i.e. NOx, CO, NMVOCs (non-methane volatile organic compounds), SOx, NH3, PM10 and PM2.5) and greenhouse gases (i.e. CO2 and CH4, only related to combustion processes). Specific emission estimation methodologies are provided for each source and are mostly based on (but not limited to) the calculation methodologies reported by the European EMEP/EEA air pollutant emission inventory guidebook. Meteorologically dependent functions are also included to take into account the dynamical component of the emission processes. The model also provides several functionalities for automatically manipulating and performing spatial operations on georeferenced objects (shapefiles and raster files). The model is designed so that it can be applicable to any European country or region where the required input data are available. As in the case of the global_regional module, emissions can be estimated on several user-defined grids, mapped to multiple chemical mechanisms and adapted to the input requirements of different atmospheric chemistry models (CMAQ, WRF-Chem and MONARCH) as well as a street-level dispersion model (R-LINE). Specific emission outputs generated by the model are presented and discussed to illustrate its capabilities.

scholarly journals Decoupling of urban CO<sub>2</sub> and air pollutant emission reductions during the European SARS-CoV-2 lockdown

2021 ◽  
Vol 21 (4) ◽  
pp. 3091-3102
Author(s):  
Christian Lamprecht ◽  
Martin Graus ◽  
Marcus Striednig ◽  
Michael Stichaner ◽  
Thomas Karl
Keyword(s):  
Air Quality ◽  
Energy Demand ◽  
Air Pollutant ◽  
Mitigation Measures ◽  
Pollutant Emission ◽  
Systematic Bias ◽  
Sensitivity Experiment ◽  
Emission Models ◽  
Time Changes ◽  
Pollution Emissions

Abstract. Lockdown and the associated massive reduction in people's mobility imposed by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) mitigation measures across the globe provide a unique sensitivity experiment to investigate impacts on carbon and air pollution emissions. We present an integrated observational analysis based on long-term in situ multispecies eddy flux measurements, allowing for quantifying near-real-time changes of urban surface emissions for key air quality and climate tracers. During the first European SARS-CoV-2 wave we find that the emission reduction of classic air pollutants decoupled from CO2 and was significantly larger. These differences can only be rationalized by the different nature of urban combustion sources and point towards a systematic bias of extrapolated urban NOx emissions in state-of-the-art emission models. The analysis suggests that European policies, shifting residential, public, and commercial energy demand towards cleaner combustion, have helped to improve air quality more than expected and that the urban NOx flux remains to be dominated (e.g., >90 %) by traffic.

scholarly journals Exploring Deep Learning for Air Pollutant Emission Estimation

2021 ◽  
Author(s):  
Lin Huang ◽  
Song Liu ◽  
Zeyuan Yang ◽  
Jia Xing ◽  
Jia Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Emission Inventory ◽  
High Efficiency ◽  
Rapid Development ◽  
Air Pollutant ◽  
Pollutant Emission ◽  
Good Representation ◽  
Air Quality Model ◽  
Quality Model

Abstract. The inaccuracy of anthropogenic emission inventory on a high-resolution scale due to insufficient basic data is one of the major reasons for the deviation between air quality model and observation results. A bottom-up approach, as a typical emission inventory estimation approach, requires a lot of human labor and material resources, and a top-down approach focuses on individual pollutants that can be measured directly and relies heavily on traditional numerical modelling. Lately, deep neural network has achieved rapid development due to its high efficiency and non-linear expression ability. In this study, we proposed a novel method to model the dual relationship between emission inventory and pollution concentration for emission inventory estimation. Specifically, we utilized a neural network based comprehensive chemical transport model (NN-CTM) to learn the complex correlation between emission and air pollution. We further updated the emission inventory based on backpropagating the gradient of the loss function measuring the deviation between NN-CTM and observations from surface monitors. We first mimicked the CTM model with neural networks (NN) and achieved a relatively good representation of CTM with similarity reaching 95 %. To reduce the gap between CTM and observations, the NN model would suggest an updated emission of NOx, NH3, SO2, VOC and primary PM2.5 which changes by −1.34 %, −2.65 %, −11.66 %, −19.19 % and 3.51 %, respectively, on average of China. Such ratios of NOx and PM2.5 are even higher (~10 %) particularly in Northwest China where suffers from large uncertainties in original emissions. The updated emission inventory can improve model performance and make it closer to observations. The mean absolute error for NO2, SO2, O3 and PM2.5 concentrations are reduced significantly by about 10 %~20 %, indicating the high feasibility of NN-CTM in terms of significantly improving both the accuracy of emission inventory as well as the performance of air quality model.

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