scholarly journals Evaluate of air pollution dispersion and propose planing scenerios to reduce air pollution for livestock activities in Tan Thanh district, Ba Ria – Vung Tau province

2019 ◽  
Vol 2 (2) ◽  
pp. 26-37 ◽  
Author(s):  
Dung Minh Ho ◽  
Bang Quoc Ho ◽  
Thang Viet Le
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Agricultural Sector ◽  
Ambient Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Meteorological Model ◽  
Pollution Dispersion ◽  
Air Pollution Dispersion

Livestock is one of the main activities of the agricultural sector in Tan Thanh district, Ba Ria – Vung Tau province. Beside of pollution sources such as waste water, solid waste, livestock activity in Tan Thanh district, Ba Ria - Vung Tau province in recent years has caused air pollution in the livestock area and surrounding area. This research was carried out to evaluate the process of air pollution dispersion from livestock activities based on applying the TAPM meteorological model and AERMOD air quality model. The results showed that the maximum concentrations of air pollutants from livestock area such as NH3, H2S and CH3SH exceeded the National Technical Regulation on Ambient Air Quality (average hour) in the centre of Tan Thanh district, such as Toc Tien commune, part of Tan Phuoc and Phuoc Hoa communes, is 505 μg/m3; 57.4 μg/m3 and 111 μg/m3, respectively. Phu My district and other suburban communes (Hac Dich, Song Xoai, Chau Pha, Tan Hoa, Tan Hai, My Xuan, etc.) have distribution of lower concentrations of air pollutants. Base on the present results of modeling, the authors have proposed livestock development scenarios to control air pollution from this activity, contributing to environmental protection for Tan Thanh district.

scholarly journals Forming High Ozone Concentration in the Ambient Air of Southern Taiwan under the Effects of Western Pacific Subtropical High

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Kuo-Cheng Lo ◽  
Chung-Hsuang Hung
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Model Simulation ◽  
Western Pacific Subtropical High ◽  
Ambient Air ◽  
Western Pacific ◽  
Subtropical High ◽  
Air Quality Model ◽  
Quality Model ◽  
Southern Taiwan

Due to the distinct geographical and meteorological conditions of Taiwan, air pollutants concentrations in the ambient air of it may vary with seasons. Accordingly, this study aimed to investigate the formation of high O3concentration in the ambient air of Southern Taiwan during summers. A high O3concentration case occurring between June 28 and July 2, 2013, was modeled and analyzed with WRF-Chem meteorological and air quality model. During the investigated period, a typical western Pacific subtropical high (WPSH) covered most East Asia, including Taiwan and its surrounding areas. The observations showed strong correlations between WPSH invasion and forming high O3concentrations. The dispersion of air pollutants in the ambient air is not sufficient to dilute their concentrations. In the afternoon of June 30, more than 60% of the air quality monitoring stations found O3concentrations exceeding 100 ppb, which were 2~3 times higher than their normal concentrations. Model simulation results verified that the presence of the WPSH hindered the dilution and transportation of air pollutants in ambient air. In addition, the air quality would be getting worse due to the leeward sides caused by the counter clockwise vertex formed in Southwestern Taiwan.

scholarly journals Analysis of Air Quality Impacts on Human Health Using the Geoinformatics Application: Chiang Rai Province

2017 ◽  
pp. 25-32
Author(s):  
Anuttara Hongthong ◽  
Yanasinee Suma ◽  
Nittaya Pasukphun ◽  
Vivat Keawdounglek
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Relative Humidity ◽  
Air Pollutants ◽  
Ambient Air ◽  
Temporal Analysis ◽  
City Centre ◽  
Pollution Dispersion ◽  
Public Health Office ◽  
Lower Temperature

This research aims to study air pollution dispersion in Chiang Rai Province, Thailand. The relationship between air pollutants, meteorology and population health were considered. The levels of air pollutants were used to establish a spatial and temporal analysis by Inverse Distance Weighted (IDW) interpolation from Geographic Information Systems (GIS), involved with occurrences of disease cases in the study area. The average monthly air pollution data were collected from Thailand’s Pollution Control Department and data on respiratory disease were collected from Chiang Rai Provincial Public Health Office during 2011 to 2014. The results indicated that monthly average PM10 concentrations started to rise from December to April. PM10 concentrations peaked during the hot season of every year, when open burning is prac-ticed. During this period, PM10 levels exceeded Thailand’s national ambient air quality standardsof 120 μg m-3. Accumulative influenza and pneumonia cases in Chiang Rai Province were very high in Chiang Rai city centre. The spatial temperature distribution map showed higher incidence of cases of influenza and pneumonia throughout the lower temperature area of Chiang Rai city centre. Influenza was affected by PM10, rainfall, relative humidity, and temperature, according to the following correlation ratios: 0.8217, 0.8842, 0.9375 and 0.8775, respectively. The incidence of pneumonia was affected by rainfall, relative humidity and temperature following the correlation ratios 0.7746, 0.7621 and 0.9684, respectively. Whereas PM10 was low associated with pneumonia as a significant ratio was 0.6079. Pneumonia incidence decreased when rainfall and temperature decreased, and increased when relative humidity increased.

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 Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

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.

The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks

2008 ◽  
Vol 47 (7) ◽  
pp. 1853-1867 ◽  
Author(s):  
Tanya L. Otte
Keyword(s):  
Air Quality ◽  
Meteorological Conditions ◽  
Daily Maximum ◽  
Air Quality Model ◽  
Quality Model ◽  
Meteorological Model ◽  
Model Simulations ◽  
Dynamic Analyses ◽  
The Impact ◽  
Over Time

Abstract It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.

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