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 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.

scholarly journals Exploring deep learning for air pollutant emission estimation

2021 ◽  
Vol 14 (7) ◽  
pp. 4641-4654
Author(s):  
Lin Huang ◽  
Song Liu ◽  
Zeyuan Yang ◽  
Jia Xing ◽  
Jia Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Emission Inventory ◽  
Rapid Development ◽  
Estimation Method ◽  
Air Pollutant ◽  
Pollutant Emission ◽  
Good Representation ◽  
Air Quality Model ◽  
Quality Model

Abstract. The inaccuracy of anthropogenic emission inventories 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, which is a typical emission inventory estimation method, requires a lot of human labor and material resources, whereas a top-down approach focuses on individual pollutants that can be measured directly as well as relying heavily on traditional numerical modeling. Lately, the deep neural network approach has achieved rapid development due to its high efficiency and nonlinear expression ability. In this study, we proposed a novel method to model the dual relationship between an emission inventory and pollution concentrations for emission inventory estimation. Specifically, we utilized a neural-network-based comprehensive chemical transport model (NN-CTM) to explore the complex correlation between emission and air pollution. We further updated the emission inventory based on back-propagating 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 (NNs) and achieved a relatively good representation of the CTM, with similarity reaching 95 %. To reduce the gap between the CTM and observations, the NN model suggests updated emissions of NOx, NH3, SO2, volatile organic compounds (VOCs) and primary PM2.5 changing, on average, by −1.34 %, −2.65 %, −11.66 %, −19.19 % and 3.51 %, respectively, in China for 2015. Such ratios of NOx and PM2.5 are even higher (∼ 10 %) in regions that suffer from large uncertainties in original emissions, such as Northwest China. 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 the emission inventory and the performance of the air quality model.

scholarly journals Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0

2013 ◽  
Vol 6 (4) ◽  
pp. 883-899 ◽  
Author(s):  
K. W. Appel ◽  
G. A. Pouliot ◽  
H. Simon ◽  
G. Sarwar ◽  
H. O. T. Pye ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Trace Metals ◽  
Urban Areas ◽  
Temporal Distribution ◽  
Air Pollutant ◽  
Eastern United States ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Version

Abstract. The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport, and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the model-derived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface-based measurements from several air quality networks. The CMAQ modeling system overall did well replicating the observed soil concentrations in the western United States (mean bias generally around ±0.5 μg m−3); however, the model consistently overestimated the observed soil concentrations in the eastern United States (mean bias generally between 0.5–1.5 μg m−3), regardless of season. The performance of the individual trace metals was highly dependent on the network, species, and season, with relatively small biases for Fe, Al, Si, and Ti throughout the year at the Interagency Monitoring of Protected Visual Environments (IMPROVE) sites, while Ca, K, and Mn were overestimated and Mg underestimated. For the urban Chemical Speciation Network (CSN) sites, Fe, Mg, and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al (380%), Ti (370%) and Si (470%) in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust-related emission sources in the emissions inventory.

scholarly journals Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0

2013 ◽  
Vol 6 (1) ◽  
pp. 1859-1899 ◽  
Author(s):  
K. W. Appel ◽  
G. A. Pouliot ◽  
H. Simon ◽  
G. Sarwar ◽  
H. O. T. Pye ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Trace Metals ◽  
Urban Areas ◽  
Temporal Distribution ◽  
Air Pollutant ◽  
Eastern United States ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Version

Abstract. The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the model-derived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface based measurements from several air quality networks. The CMAQ modeling system generally did well replicating the observed soil concentrations in the western United States; however the model consistently overestimated the observed soil concentrations in the eastern United States, regardless of season. The performance of the individual trace metals was generally good at the rural network sites, with relatively small biases for Fe, Al, Si and Ti throughout the year, while Ca, K and Mn were overestimated and Mg underestimated. For the urban sites, Fe, Mg and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al, Ti and Si in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust related emissions sources in the emissions inventory.

scholarly journals New approach for optimal electricity planning and dispatching with hourly time-scale air quality and health considerations

2015 ◽  
Vol 112 (35) ◽  
pp. 10884-10889 ◽  
Author(s):  
Paul Y. Kerl ◽  
Wenxian Zhang ◽  
Juan B. Moreno-Cruz ◽  
Athanasios Nenes ◽  
Matthew J. Realff ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plant ◽  
Power Plants ◽  
Air Pollutant ◽  
Health Impacts ◽  
Reduced Form ◽  
Air Quality Model ◽  
Quality Model ◽  
Electricity Planning

Integrating accurate air quality modeling with decision making is hampered by complex atmospheric physics and chemistry and its coupling with atmospheric transport. Existing approaches to model the physics and chemistry accurately lead to significant computational burdens in computing the response of atmospheric concentrations to changes in emissions profiles. By integrating a reduced form of a fully coupled atmospheric model within a unit commitment optimization model, we allow, for the first time to our knowledge, a fully dynamical approach toward electricity planning that accurately and rapidly minimizes both cost and health impacts. The reduced-form model captures the response of spatially resolved air pollutant concentrations to changes in electricity-generating plant emissions on an hourly basis with accuracy comparable to a comprehensive air quality model. The integrated model allows for the inclusion of human health impacts into cost-based decisions for power plant operation. We use the new capability in a case study of the state of Georgia over the years of 2004–2011, and show that a shift in utilization among existing power plants during selected hourly periods could have provided a health cost savings of $175.9 million dollars for an additional electricity generation cost of $83.6 million in 2007 US dollars (USD2007). The case study illustrates how air pollutant health impacts can be cost-effectively minimized by intelligently modulating power plant operations over multihour periods, without implementing additional emissions control technologies.

scholarly journals Sensitivity of air quality modelling to different emission inventories: a case study over Europe

2020 ◽  
Author(s):  
Philippe Thunis ◽  
Monica Crippa ◽  
Cornelis Cuvelier ◽  
Diego Guizzardi ◽  
Alexander De Meij ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutant ◽  
Activity Level ◽  
Pollutant Emissions ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Air Quality Modelling ◽  
Quality Model ◽  
Yearly Average ◽  
Global Coverage

Abstract. In this paper we run the EMEP air quality model with 3 different emission inventories at 2015, that is to say using CAMS, EMEP emissions, and EDGAR. The EMEP model has been run for the entire year 2015, and resulting concentration results have been compared with background monitoring stations. Results show that the air quality model, run with the 3 emission inventories, provide similar results despite the emission differences. More in details, EDGAR is providing slightly better validation results for PM2.5, while the EMEP emissions are slightly better to model yearly average NO2. The main differences among the model applications arise in the Eastern part of Europe, where the values between the officially estimated emissions and those independently estimated by EDGAR are higher. Results suggest that EDGAR, despite being a methodology aimed at global coverage, with independent sources for activity level, technologies and emission factors and generic gridding practices, can be effectively used for air quality modelling in Europe. The EDGAR dataset (v5.0) used in this paper is available at: https://doi.org/10.2904/JRC_DATASET_EDGAR (link: EDGAR v5.0 Global Air Pollutant Emissions, Crippa et al., 2020a).

Using CMAQ for Exposure Modeling and Characterizing the Subgrid Variability for Exposure Estimates

2007 ◽  
Vol 46 (9) ◽  
pp. 1354-1371 ◽  
Author(s):  
Vlad Isakov ◽  
John S. Irwin ◽  
Jason Ching
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Dispersion Model ◽  
Air Pollutant ◽  
Atmospheric Pollutants ◽  
Exposure Model ◽  
Air Toxics ◽  
Air Quality Model ◽  
Quality Model ◽  
Discrete Location

Abstract Atmospheric processes and the associated transport and dispersion of atmospheric pollutants are known to be highly variable in time and space. Current air-quality models that characterize atmospheric chemistry effects, for example, the Community Multiscale Air Quality model (CMAQ), provide volume-averaged concentration values for each grid cell in the modeling domain given the stated conditions. Given the assumptions made and the limited set of processes included in any model’s implementation, there are many sources of “unresolved” subgrid variability. This raises the question of the importance of the unresolved subgrid variations on exposure assessment results if such models were to be used to assess air toxics exposure. In this study, the Hazardous Air Pollutant Exposure Model (HAPEM) is applied to estimate benzene and formaldehyde inhalation exposure using ambient annually averaged concentrations predicted by CMAQ to investigate how within-grid variability can affect exposure estimates. An urban plume dispersion model was used to estimate the subgrid variability of annually averaged benzene concentration values within CMAQ grid cells for a modeling domain centered on Philadelphia, Pennsylvania. Significant (greater than a factor of 2) increases in maximum exposure impacts were seen in the exposure estimates in comparison with exposure estimates generated using CMAQ grid-averaged concentration values. These results consider only one source of subgrid variability, namely, the discrete location and distribution of emissions, but they do suggest the importance and value of developing improved characterizations of subgrid concentration variability for use in air toxics exposure assessments.

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