scholarly journals Overview of Model Inter-Comparison in Japan’s Study for Reference Air Quality Modeling (J-STREAM)

Atmosphere ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 19 ◽  
Author(s):  
Satoru Chatani ◽  
Kazuyo Yamaji ◽  
Tatsuya Sakurai ◽  
Syuichi Itahashi ◽  
Hikari Shimadera ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Three Dimensional ◽  
Model Performance ◽  
Air Quality Modeling ◽  
Second Phase ◽  
Common Input ◽  
Quality Modeling ◽  
Photochemical Ozone ◽  
Secondary Air

The inter-comparison of regional air quality models is an effective way to understand uncertainty in ambient pollutant concentrations simulated using various model configurations, as well as to find ways to improve model performance. Based on the outcomes and experiences of Japanese projects thus far, a new model inter-comparison project called Japan’s study for reference air quality modeling (J-STREAM) has begun. The objective of J-STREAM is to establish reference air quality modeling for source apportionment and effective strategy making to suppress secondary air pollutants including PM2.5 and photochemical ozone in Japan through model inter-comparison. The first phase focuses on understanding the ranges and limitations in ambient PM2.5 and ozone concentrations simulated by participants using common input datasets. The second phase focuses on issues revealed in previous studies in simulating secondary inorganic aerosols, as well as on the three-dimensional characteristics of photochemical ozone as a new target. The third phase focuses on comparing source apportionments and sensitivities under heavy air pollution episodes simulated by participating models. Detailed understanding of model performance, uncertainty, and possible improvements to urban-scale air pollution involving secondary pollutants, as well as detailed sector-wise source apportionments over megacities in Japan are expected.

scholarly journals Air quality forecast of PM<sub>10</sub> in Beijing with Community Multi-scale Air Quality Modeling (CMAQ) system: emission and improvement

2014 ◽  
Vol 7 (5) ◽  
pp. 2243-2259 ◽  
Author(s):  
Q. Z. Wu ◽  
W. S. Xu ◽  
A. J. Shi ◽  
Y. T. Li ◽  
X. J. Zhao ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Correlation Coefficient ◽  
Model Performance ◽  
Air Quality Modeling ◽  
Area Source ◽  
Model Domain ◽  
Pollution Episode ◽  
Domain Method ◽  
Quality Modeling

Abstract. The MM5-SMOKE-CMAQ model system, which was developed by the United States Environmental Protection Agency (US EPA) as the MODELS-3 system, has been used for daily air quality forecasts in the Beijing Municipal Environmental Monitoring Center (Beijing MEMC), as a part of the Ensemble air quality Modeling forecast System for Beijing (EMS-Beijing) since the 2008 Olympic Games. According to the daily forecast results for the entire duration of 2010, the model shows good performance in the PM10 forecast on most days but clearly underestimates PM10 concentration during some air pollution episodes. A typical air pollution episode from 11–20 January 2010 was chosen, in which the observed air pollution index of particulate matter (PM10-API) reached 180 while the forecast PM10-API was about 100. In this study, three numerical methods are used for model improvement: first, by enhancing the inner domain with 3 km resolution grids, and expanding the coverage from only Beijing to an area including Beijing and its surrounding cities; second, by adding more regional point source emissions located at Baoding, Landfang and Tangshan, to the south and east of Beijing; third, by updating the area source emissions, including the regional area source emissions in Baoding and Tangshan and the local village/town-level area source emissions in Beijing. The last two methods are combined as the updated emissions method. According to the model sensitivity testing results by the CMAQ model, the updated emissions method and expanded model domain method can both improve the model performance separately. But the expanded model domain method has better ability to capture the peak values of PM10 than the updated emissions method due to better reproduction of the pollution transport process in this episode. As a result, the hindcast results ("New(CMAQ)"), which are driven by the updated emissions in the expanded model domain, show a much better model performance in the national standard station-averaged PM10-API. The daily hindcast PM10-API reaches 180 and is much closer to the observed value, and has a high correlation coefficient of 0.93. The correlation coefficient of the PM10-API in all Beijing MEMC stations between the hindcast and observation is 0.82, clearly higher than the forecast 0.54. The FAC2 increases from 56% in the forecast to 84% in the hindcast, and the NMSE decreases from 0.886 to 0.196. The hindcast also has better model performance in PM10 hourly concentrations during the typical air pollution episode. The updated emissions method accompanied by a suitable domain in this study improved the model performance for the Beijing area significantly.

scholarly journals Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region

2021 ◽  
Vol 18 (8) ◽  
pp. 4155
Author(s):  
Diogo Lopes ◽  
Joana Ferreira ◽  
Ka In Hoi ◽  
Ka-Veng Yuen ◽  
Kai Meng Mok ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pearl River Delta ◽  
Air Quality Modeling ◽  
Pearl River ◽  
Emission Inventories ◽  
Atmospheric Emission ◽  
The Pearl River Delta ◽  
Quality Modeling ◽  
The Pearl River

The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study’s comparison of the three emission inventories’ data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.

Air Quality Modeling and Metamodeling Approach

2011 ◽  
pp. 144-161 ◽  
Author(s):  
Miloslava Kašparová ◽  
Jirí Krupka
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Classification Problem ◽  
Air Quality Modeling ◽  
Point Of View ◽  
Interaction Detection ◽  
Power Stations ◽  
Quality Modeling ◽  
Chi Squared ◽  
Problematic Area

This chapter deals with modeling and metamodeling of air quality in the Pardubice region of the Czech Republic. From a regional point of view, the Pardubice district is the most problematic area in regards to air pollution. Concentrations of traffic, industry and power stations (Opatovice and Chvaletice) activities are the cause of this situation, although emissions of all pollutants have markedly decreased within the last ten years. A decrease in air pollution was achieved particularly by restriction and restructuring of industrial production, use of emission standards, changes in legislation in the area of air protection, etc. The mentioned air quality modeling belongs to classification tasks. It means the authors deal with the classification problem, with the creation of classification models (classifiers) and they focus on metamodeling (combining classifiers). Through the application of modeling and metamodeling the authors use selected algorithms of decision trees (C5.0, chi-squared automatic interaction detection and classification and regression trees) that belong to useful explanatory techniques.

scholarly journals Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 750
Author(s):  
Hoang Ngoc Khue Vu ◽  
Quang Phuc Ha ◽  
Duc Hiep Nguyen ◽  
Thi Thu Thuy Nguyen ◽  
Thoai Tam Nguyen ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Ambient Air ◽  
Air Quality Modeling ◽  
World Health ◽  
Ho Chi Minh City ◽  
Annual Average ◽  
Quality Modeling ◽  
Concentration Of Air Pollutants

Along with its rapid urban development, Ho Chi Minh City (HCMC) in recent years has suffered a high concentration of air pollutants, especially fine particulate matters or PM2.5. A comprehensive study is required to evaluate the air quality conditions and their health impact in this city. Given the lack of adequate air quality monitoring data over a large area of the size of HCMC, an air quality modeling methodology is adopted to address the requirement. Here, by utilizing a corresponding emission inventory in combination with The Air Pollution Model-Chemical Transport Model (TAPM-CTM), the predicted concentration of air pollutants is first obtained for PM2.5, NOx, and SO2. Then by associating the pollutants exposed with the mortality rate from three causes, namely Ischemic Heart Disease (IHD), cardiopulmonary, and lung cancer, the impact of air pollution on human health is obtained for this purpose. Spatial distribution has shown a high amount of pollutants concentrated in the central city with a high density of combustion vehicles (motorcycles and automobiles). In addition, a significant amount of emissions can be observed from stevedoring and harbor activities, including ferries and cargo handling equipment located along the river. Other sources such as household activities also contribute to an even distribution of emission across the city. The results of air quality modeling showed that the annual average concentrations of NO2 were higher than the standard of Vietnam National Technical Regulation on Ambient Air Quality (QCVN 05: 2013 40 µg/m3) and World Health Organization (WHO) (40 µg/m3). The annual average concentrations of PM2.5 were 23 µg/m3 and were also much higher than the WHO (10 µg/m3) standard by about 2.3 times. In terms of public health impacts, PM2.5 was found to be responsible for about 1136 deaths, while the number of mortalities from exposure to NO2 and SO2 was 172 and 89 deaths, respectively. These figures demand some stringent measures from the authorities to potentially remedy the alarming situation of air pollution in HCM City.

Air pollution emission inventory and air quality modeling for Can Tho City, Mekong Delta, Vietnam

2017 ◽  
Vol 11 (1) ◽  
pp. 35-47 ◽  
Author(s):  
Ho Quoc Bang ◽  
Vu Hoang Ngoc Khue ◽  
Nguyen Thoai Tam ◽  
Kristofer Lasko
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Emission Inventory ◽  
Mekong Delta ◽  
Air Quality Modeling ◽  
Pollution Emission ◽  
Quality Modeling ◽  
Can Tho City

scholarly journals Global, High-Resolution, Reduced-Complexity Air Quality Modeling Using InMAP (Intervention Model for Air Pollution)

2021 ◽  
Author(s):  
Sumil Thakrar ◽  
Christopher Tessum ◽  
Joshua Apte ◽  
Srinidhi Balasubramanian ◽  
Dylan B Millet ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Transport Model ◽  
The United States ◽  
Air Quality Modeling ◽  
Air Quality Model ◽  
Desktop Computer ◽  
Policy Assessment ◽  
Quality Modeling ◽  
Reduced Complexity

Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many policy scenarios at high spatial resolution. However, air quality modeling typically has high requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with 4 km horizontal grid cell widths in cities. We evaluate Global InMAP performance both against measurements and a state-of-the-science chemical transport model, GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6–4.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate air pollution policy assessment worldwide, providing a screening tool for reducing the deaths where they occur most.

scholarly journals Air quality modeling with WRF-Chem v3.5 in East Asia: sensitivity to emissions and evaluation of simulated air quality

2016 ◽  
Vol 9 (3) ◽  
pp. 1201-1218 ◽  
Author(s):  
Min Zhong ◽  
Eri Saikawa ◽  
Yang Liu ◽  
Vaishali Naik ◽  
Larry W. Horowitz ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Model Performance ◽  
Air Quality Modeling ◽  
Performance Criteria ◽  
Air Quality Model ◽  
Quality Model ◽  
Provincial Level ◽  
Mixing Ratios ◽  
Quality Modeling

Abstract. We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East Asia at a spatial resolution of 20 km  ×  20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in ASia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences of up to 500 % for CO emissions, 190 % for NO, and 160 % for primary PM10. Such discrepancies in the magnitude and the spatial distribution of emissions for various species lead to a 40–70 % difference in surface PM10 concentrations, 16–20 % in surface O3 mixing ratios, and over 100 % in SO2 and NO2 mixing ratios in the polluted areas of China. WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct additional model simulations using REAS emissions for January, April, July, and October of 2007 and evaluate simulations with available ground-level observations. The model results illustrate clear regional variations in the seasonal cycle of surface PM10 and O3 over East Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB ⩽ ±60 %) and O3 (MFB ⩽ ±15 %) at most of the observation sites, although the model underestimates PM10 over northeastern China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates observed NO2 in all 4 months. Our study highlights the importance of constraining emissions at the provincial level for regional air quality modeling over East Asia. Our results suggest that future work should focus on the improvement of provincial-level emissions especially estimating primary PM, SO2, and NOx.

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