scholarly journals Numerical Simulation of an Air Pollution Model on Industrial Areas by Considering the Influence of Multiple Point Sources

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Pravitra Oyjinda ◽  
Nopparat Pochai
Keyword(s):  
Numerical Simulation ◽  
Air Pollution ◽  
Approximate Solutions ◽  
Measurement Model ◽  
Point Sources ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Multiple Point ◽  
Industrial Zone ◽  
Pollution Measurement

A numerical simulation on a two-dimensional atmospheric diffusion equation of an air pollution measurement model is proposed. The considered area is separated into two parts that are an industrial zone and an urban zone. In this research, the air pollution measurement by releasing the pollutant from multiple point sources above an industrial zone to the other area is simulated. The governing partial differential equation of air pollutant concentration is approximated by using a finite difference technique. The approximate solutions of the air pollutant concentration on both areas are compared. The air pollutant concentration levels influenced by multiple point sources are also analyzed.

scholarly journals Numerical Simulation for a Three-Dimensional Air Pollution Measurement Model in a Heavy Traffic Area under the Bangkok Sky Train Platform

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Kewalee Suebyat ◽  
Nopparat Pochai
Keyword(s):  
Numerical Simulation ◽  
Air Pollution ◽  
Heavy Traffic ◽  
Three Dimensional ◽  
Measurement Model ◽  
Air Pollutant ◽  
Traffic Area ◽  
Pollutant Sources ◽  
Pollution Measurement ◽  
High Traffic Volume

Air pollutant levels in Bangkok are generally high in street tunnels. They are particularly elevated in almost closed street tunnels such as an area under the Bangkok sky train platform with high traffic volume where dispersion is limited. There are no air quality measurement stations in the vicinity, while the human population is high. In this research, the numerical simulation is used to measure the air pollutant levels. The three-dimensional air pollution measurement model in a heavy traffic area under the Bangkok sky train platform is proposed. The finite difference techniques are employed to approximate the modelled solutions. The vehicle air pollutant emission due to the high traffic volume is mathematically assumed by the pollutant sources term. The simulation is also considered in averaged and moving pollutant sources due to manner vehicle emission. The proposed approximated air pollutant concentration indicators can be replaced by user required gaseous pollutants indices such as NOx, SO2, CO, and PM2.5.

scholarly journals Numerical Simulation to Air Pollution Emission Control near an Industrial Zone

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Pravitra Oyjinda ◽  
Nopparat Pochai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Industrial Development ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Air Pollution Control ◽  
Industrial Zone ◽  
Concentration Data ◽  
Monitoring Point ◽  
Concentration Levels

A rapid industrial development causes several environment pollution problems. One of the main problems is air pollution, which affects human health and the environment. The consideration of an air pollutant has to focus on a polluted source. An industrial factory is an important reason that releases the air pollutant into the atmosphere. Thus a mathematical model, an atmospheric diffusion model, is used to estimate air quality that can be used to describe the sulfur dioxide dispersion. In this research, numerical simulations to air pollution measurement near industrial zone are proposed. The air pollution control strategies are simulated to achieve desired pollutant concentration levels. The monitoring points are installed to detect the air pollution concentration data. The numerical experiment of air pollution consisted of different situations such as normal and controlled emissions. The air pollutant concentration is approximated by using an explicit finite difference technique. The solutions of calculated air pollutant concentration in each controlled and uncontrolled point source at the monitoring points are compared. The air pollutant concentration levels for each monitoring point are controlled to be at or below the national air quality standard near industrial zone index.

scholarly journals MODELLING AND SIMULATION OF DISPERSIONS OF POWDER EMISSIONS FROM MULTIPLE SOURCES WITH THE MATHEMATICAL MODEL POL 15SM

2014 ◽  
Vol 22 (2) ◽  
pp. 151-160
Author(s):  
Mihaela Budianu ◽  
Valeriu Nagacevschi ◽  
Matei Macoveanu
Keyword(s):  
Mathematical Model ◽  
Air Pollution ◽  
Urban Areas ◽  
Point Sources ◽  
Multiple Point ◽  
Multiple Sources ◽  
Economic Agents ◽  
Sources Of Pollution ◽  
Simulation Based ◽  
The Mathematical Model

Over the last decades, air pollution has become one of the greatest challenges negatively affecting human health and the entire environment, including air, water, soil, vegetation, and urban areas. Lately, special attention has been given to mathematical modelling for diffusion of pollutants in the atmosphere as a particularly effective and efficient method that can be used to study, control and reduce air pollution. The diversity of models developed by different research groups imposed a rigorous understanding of model types in order to apply them correctly according to local or regional problems of air pollution phenomenon. Tus the authors have developed and improved two mathematical models for dispersion of air pollutants. Tis paper presents a case study of dispersion of powders in suspension originating from 14 point sources that correspond to 5 economic agents in the agroindustrial area of Vaslui city using a computer simulation based on the mathematical model Pol 15sm, for multiple point sources of pollution, designed by the authors.

scholarly journals Association between exposure to ambient air pollution and hospital admission, incidence, and mortality of stroke: an updated systematic review and meta-analysis of more than 23 million participants

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Zhiping Niu ◽  
Feifei Liu ◽  
Hongmei Yu ◽  
Shaotang Wu ◽  
Hao Xiang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Hospital Admission ◽  
Air Pollutants ◽  
Meta Analysis ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Aerodynamic Diameter ◽  
Stroke Incidence ◽  
Incidence And Mortality

Abstract Background Previous studies have suggested that exposure to air pollution may increase stroke risk, but the results remain inconsistent. Evidence of more recent studies is highly warranted, especially gas air pollutants. Methods We searched PubMed, Embase, and Web of Science to identify studies till February 2020 and conducted a meta-analysis on the association between air pollution (PM2.5, particulate matter with aerodynamic diameter less than 2.5 μm; PM10, particulate matter with aerodynamic diameter less than 10 μm; NO2, nitrogen dioxide; SO2, sulfur dioxide; CO, carbon monoxide; O3, ozone) and stroke (hospital admission, incidence, and mortality). Fixed- or random-effects model was used to calculate pooled odds ratios (OR)/hazard ratio (HR) and their 95% confidence intervals (CI) for a 10 μg/m3 increase in air pollutant concentration. Results A total of 68 studies conducted from more than 23 million participants were included in our meta-analysis. Meta-analyses showed significant associations of all six air pollutants and stroke hospital admission (e.g., PM2.5: OR = 1.008 (95% CI 1.005, 1.011); NO2: OR = 1.023 (95% CI 1.015, 1.030), per 10 μg/m3 increases in air pollutant concentration). Exposure to PM2.5, SO2, and NO2 was associated with increased risks of stroke incidence (PM2.5: HR = 1.048 (95% CI 1.020, 1.076); SO2: HR = 1.002 (95% CI 1.000, 1.003); NO2: HR = 1.002 (95% CI 1.000, 1.003), respectively). However, no significant differences were found in associations of PM10, CO, O3, and stroke incidence. Except for CO and O3, we found that higher level of air pollution (PM2.5, PM10, SO2, and NO2) exposure was associated with higher stroke mortality (e.g., PM10: OR = 1.006 (95% CI 1.003, 1.010), SO2: OR = 1.006 (95% CI 1.005, 1.008). Conclusions Exposure to air pollution was positively associated with an increased risk of stroke hospital admission (PM2.5, PM10, SO2, NO2, CO, and O3), incidence (PM2.5, SO2, and NO2), and mortality (PM2.5, PM10, SO2, and NO2). Our study would provide a more comprehensive evidence of air pollution and stroke, especially SO2 and NO2.

scholarly journals Prediction of Air Pollutant Concentration Based on One-Dimensional Multi-Scale CNN-LSTM Considering Spatial-Temporal Characteristics: A Case Study of Xi’an, China

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1626
Author(s):  
Hongbin Dai ◽  
Guangqiu Huang ◽  
Jingjing Wang ◽  
Huibin Zeng ◽  
Fangyu Zhou
Keyword(s):  
Air Pollution ◽  
Air Pollutants ◽  
Prediction Models ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Concentration Data ◽  
Spatial Characteristics ◽  
Data Prediction ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics

Air pollution has become a serious problem threatening human health. Effective prediction models can help reduce the adverse effects of air pollutants. Accurate predictions of air pollutant concentration can provide a scientific basis for air pollution prevention and control. However, the previous air pollution-related prediction models mainly processed air quality prediction, or the prediction of a single or two air pollutants. Meanwhile, the temporal and spatial characteristics and multiple factors of pollutants were not fully considered. Herein, we establish a deep learning model for an atmospheric pollutant memory network (LSTM) by both applying the one-dimensional multi-scale convolution kernel (ODMSCNN) and a long-short-term memory network (LSTM) on the basis of temporal and spatial characteristics. The temporal and spatial characteristics combine the respective advantages of CNN and LSTM networks. First, ODMSCNN is utilized to extract the temporal and spatial characteristics of air pollutant-related data to form a feature vector, and then the feature vector is input into the LSTM network to predict the concentration of air pollutants. The data set comes from the daily concentration data and hourly concentration data of six atmospheric pollutants (PM2.5, PM10, NO2, CO, O3, SO2) and 17 types of meteorological data in Xi’an. Daily concentration data prediction, hourly concentration data prediction, group data prediction and multi-factor prediction were used to verify the effectiveness of the model. In general, the air pollutant concentration prediction model based on ODMSCNN-LSTM shows a better prediction effect compared with multi-layer perceptron (MLP), CNN, and LSTM models.

scholarly journals HADOOP-BASED DISTRIBUTED SYSTEM FOR ONLINE PREDICTION OF AIR POLLUTION BASED ON SUPPORT VECTOR MACHINE

2015 ◽  
Vol XL-1-W5 ◽  
pp. 215-219 ◽  
Author(s):  
Z. Ghaemi ◽  
M. Farnaghi ◽  
A. Alimohammadi
Keyword(s):  
Air Pollution ◽  
Support Vector Machine ◽  
Data Analysis ◽  
Air Quality ◽  
Large Scale ◽  
Processing Time ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Support Vector ◽  
Prediction Problems

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

scholarly journals Spatial-Temporal Analysis of Air Pollutant Concentrations in Relation to Land Use and School Environment

2021 ◽  
Vol 36 (1) ◽  
pp. 568-582
Author(s):  
Healice Julit ◽  
Nafisah Khalid ◽  
Abdul Rauf Abdul Rasam ◽  
Mohamad Hezri Razali ◽  
Maisarah Abdul Halim
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
School Environment ◽  
Strong Relationship ◽  
Temporal Analysis ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Motor Vehicles ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Excessive exposure schoolchildren to air pollution can lead to long-lasting health problems, allergies and respiratory disease. It is well known that the major factors contributing to increase of air pollution are motor vehicles and industries. Thus, it is important to analyze the spatial temporal air pollutant concentrations and its relation with school location as the location of schools and its surrounding can increase their exposure. In this study, six schools in Johor were selected and the land use surrounding the schools were updated using ArcGIS. The Inverse Distance Weighting (IDW) interpolation technique was used to identify which schools’ area in Johor has a higher range of air pollutant concentration. There are four air pollution parameters obtained from the Department of Environment (DOE) which are PM2.5, CO, O3 and SO2. Hourly air pollutant concentration reading was obtained from the DOE in order to analyze air pollutant concentration during school period. The results obtained from the IDW technique showed that Sekolah Menengah Pasir Gudang (2) located in Pasir Gudang, Malaysia has reached a very unhealthy and hazardous level as compared to other schools in Johor. On the other hand, Sekolah Menengah Kebangsaan Tanjung Pengelih, Pengerang, Malaysia showed good to unhealthy range as compared to other schools in Johor. The spatial autocorrelation tool was used to analyze the relationship between the air pollution concentration and the school’s location in Johor. The results showed that the Moran’s Indices is positive showing a strong relationship that is clustering. It can be stated that there is a relationship between air pollutant concentrations with the school locations.

Sign in / Sign up

Export Citation Format

Share Document