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.

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 Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

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 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 Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

2015 ◽  
Vol 15 (13) ◽  
pp. 7703-7723 ◽  
Author(s):  
K. Markakis ◽  
M. Valari ◽  
O. Perrussel ◽  
O. Sanchez ◽  
C. Honore
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Horizontal Resolution ◽  
Anthropogenic Emissions ◽  
Vertical Resolution ◽  
Post Processing ◽  
Air Quality Model ◽  
Quality Model ◽  
Quality Modeling ◽  
Forced Air

Abstract. While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects, our knowledge is limited on how these uncertainties affect climate-forced air-quality assessments. Using as reference a 10-year model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (~ 8 %), the post-processing method (6.5 %) and the horizontal resolution of the air-quality model (~ 5 %), while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~ 32 %) and the resolution of the emission inventory (~ 24 %). The air-quality model horizontal and vertical resolution have little effect on model predictions for the specific study domain. In the case of modeled ozone concentrations, the implementation of refined input data results in a consistent decrease (from 2.5 up to 8.3 %), mainly due to inhibition of the titration rate by nitrogen oxides. Such consistency is not observed for PM2.5. In contrast this consistency is not observed for PM2.5. In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (~ 50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ~ 40 % by adopting a higher resolution emission inventory, by 25 % by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10 % by adopting the annual emission totals of the local inventory. The bias of PM2.5 concentrations follows a more complex pattern, with the positive values associated with the coarse run (+3.6 μg m−3), increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles, the coarse simulation cannot selectively incorporate local-scale features in order to reduce its error.

The Methodology to Establish a High Temporal Resolution Emission Inventory for Tunnel Source in City

2014 ◽  
Vol 955-959 ◽  
pp. 1380-1383
Author(s):  
Qing Huang ◽  
Hao Han ◽  
Zhan Yi Zhang ◽  
Bo Guang Wang ◽  
Chun Lin Zhang
Keyword(s):  
Air Quality ◽  
Temporal Resolution ◽  
Emission Inventory ◽  
Emission Source ◽  
Monitoring Data ◽  
High Temporal Resolution ◽  
Air Quality Model ◽  
Surrounding Area ◽  
Quality Model ◽  
Emissions Inventory

Tunnel source is an important emission source in urban microenvironment, and the influence of emission pollutants from tunnel source to air quality in surrounding area could not be ignored. In this study the monitoring data in the entrance and the exit of the tunnel was used to calculate the emission amount from tunnel source. Then the methodology to establish the tunnel source emission inventory with a high temporal resolution was discussed in this paper. This research would provide basis for the establishment of the emission inventory in urban microenvironment, and provide a more realistic emissions inventory to 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 (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.

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