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 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.

scholarly journals Emission Inventory Processing of Biomass Burning from A Global Dataset for Air Quality Modeling

2020 ◽  
Author(s):  
Ernesto Pino-Cortés ◽  
Samuel Carrasco ◽  
Luis A. Díaz-Robles ◽  
Francisco Cubillos ◽  
Fidel Vallejo ◽  
...  
Keyword(s):  
Air Quality ◽  
Biomass Burning ◽  
Emission Inventory ◽  
Sparse Matrix ◽  
Local Level ◽  
Air Quality Modeling ◽  
Simulation Software ◽  
Atmospheric Pollutants ◽  
Fire Emissions ◽  
Quality Modeling

Wildfires generate large amounts of atmospheric pollutants yearly. The development of an emissions inventory for this activity is a challenge today, mainly to perform modeling of air quality. There are free available databases with historical information about this source. The main goal of this study was to process the results of biomass burning emissions for the year 2014 from the Global Fire Assimilation System (GFAS). The pollutants studied were the black carbon, the organic carbon, fine and coarse particulate matter, respectively. The inputs were pre-formatted to enter to the simulation software of the emission inventory. In this case, the Sparse Matrix Operator Kernel Emissions (SMOKE) was used and the values obtained in various cities were analyzed. As a result, the spatial distribution of the forest fire emissions in the Southern Hemisphere was achieved, with the polar stereographic projection. The highest emissions were located in the African continent, followed by the northern region of Australia. Future air quality modeling at a local level could apply the results and the methodology of this study. The biomass burning emissions could add a better performance of the results and more knowledge on the effect of this source.

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.

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

2015 ◽  
Vol 15 (4) ◽  
pp. 4767-4821
Author(s):  
K. Markakis ◽  
M. Valari ◽  
O. Perrussel ◽  
O. Sanchez ◽  
C. Honore
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Scale Effects ◽  
Air Quality Modeling ◽  
Anthropogenic Emissions ◽  
Vertical Resolution ◽  
Model Resolution ◽  
Post Processing ◽  
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 yr 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 model resolution (~5) while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~32%) and the resolution of the emission inventory (~24%) while model horizontal and vertical resolution are of little effect. 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 on PM2.5 follows a more complex pattern with the positive bias associated to the coarse run (+3.6 μg m3) 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 model error.

scholarly journals Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu Province, China

2017 ◽  
Vol 17 (1) ◽  
pp. 211-233 ◽  
Author(s):  
Yaduan Zhou ◽  
Yu Zhao ◽  
Pan Mao ◽  
Qiang Zhang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Air Quality ◽  
High Resolution ◽  
Emission Inventory ◽  
Spatial Scales ◽  
Air Quality Modeling ◽  
Nox Emissions ◽  
Quality Modeling ◽  
Emission Estimation ◽  
Southern Jiangsu

Abstract. Improved emission inventories combining detailed source information are crucial for better understanding of the atmospheric chemistry and effectively making emission control policies using air quality simulation, particularly at regional or local scales. With the downscaled inventories directly applied, chemical transport models might not be able to reproduce the authentic evolution of atmospheric pollution processes at small spatial scales. Using the bottom-up approach, a high-resolution emission inventory was developed for Jiangsu China, including SO2, NOx, CO, NH3, volatile organic compounds (VOCs), total suspended particulates (TSP), PM10, PM2.5, black carbon (BC), organic carbon (OC), and CO2. The key parameters relevant to emission estimation for over 6000 industrial sources were investigated, compiled, and revised at plant level based on various data sources and on-site surveys. As a result, the emission fractions of point sources were significantly elevated for most species. The improvement of this provincial inventory was evaluated through comparisons with other inventories at larger spatial scales, using satellite observation and air quality modeling. Compared to the downscaled Multi-resolution Emission Inventory for China (MEIC), the spatial distribution of NOx emissions in our provincial inventory was more consistent with summer tropospheric NO2 VCDs observed from OMI, particularly for the grids with moderate emission levels, implying the improved emission estimation for small and medium industrial plants by this work. Three inventories (national, regional, and provincial by this work) were applied in the Models-3 Community Multi-scale Air Quality (CMAQ) system for southern Jiangsu October 2012, to evaluate the model performances with different emission inputs. The best agreement between available ground observation and simulation was found when the provincial inventory was applied, indicated by the smallest normalized mean bias (NMB) and normalized mean errors (NME) for all the concerned species SO2, NO2, O3, and PM2.5. The result thus implied the advantage of improved emission inventory at local scale for high-resolution air quality modeling. Under the unfavorable meteorology in which horizontal and vertical movement of atmosphere was limited, the simulated SO2 concentrations at downtown Nanjing (the capital city of Jiangsu) using the regional or national inventories were much higher than those observed, implying that the urban emissions were overestimated when economy or population densities were applied to downscale or allocate the emissions. With more accurate spatial distribution of emissions at city level, the simulated concentrations using the provincial inventory were much closer to observation. Sensitivity analysis of PM2.5 and O3 formation was conducted using the improved provincial inventory through the brute force method. Iron and steel plants and cement plants were identified as important contributors to the PM2.5 concentrations in Nanjing. The O3 formation was VOC-limited in southern Jiangsu, and the concentrations were negatively correlated with NOx emissions in urban areas owing to the accumulated NOx from transportation. More evaluations are further suggested for the impacts of speciation and temporal and vertical distribution of emissions on air quality modeling at regional or local scales in China.

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.

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