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 Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 788
Author(s):  
Rong Feng ◽  
Hongmei Xu ◽  
Zexuan Wang ◽  
Yunxuan Gu ◽  
Zhe Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Rural Areas ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Northwest China ◽  
Air Pollutant ◽  
Capital City ◽  
Air Pollution Control ◽  
Motor Vehicle Emissions

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

scholarly journals Evaluation of multivariate time series clustering for imputation of air pollution data

2021 ◽  
Vol 10 (2) ◽  
pp. 265-285
Author(s):  
Wedad Alahamade ◽  
Iain Lake ◽  
Claire E. Reeves ◽  
Beatriz De La Iglesia
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Missing Data ◽  
Multivariate Time Series ◽  
Real Data ◽  
Economic Benefits ◽  
Air Pollutant ◽  
Monitoring Site ◽  
Concentration Data

Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air-pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However, problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a multivariate time series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutant concentrations produces very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours and of the characteristics of different stations according to their environmental type.

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 Science–Policy Interplay: Improvement of Air Quality from 2008 to 2014 in Beijing and the Scientific Approach to Achieve APEC Blue

2016 ◽  
Vol 97 (4) ◽  
pp. 553-559 ◽  
Author(s):  
Zhanshan Wang ◽  
Yunting Li ◽  
Tian Chen ◽  
Dawei Zhang ◽  
Lingjun Li ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Control ◽  
Control Policy ◽  
Air Pollutant ◽  
Control Measures ◽  
Asia Pacific ◽  
Great Effort ◽  
Air Pollution Control ◽  
Scientific Approach

Abstract The Beijing government has made great effort to solve the air pollution problem in recent years. In this paper, the major air pollution control measures and the air quality improvement from 2008 to 2014 in Beijing were represented. With the implementation of a series of unconventional and high–air pollutant reduction measures in Beijing and the surrounding area, good air quality during both the 2008 Olympic Games and the 2014 Asia–Pacific Economic Cooperation (APEC) conference was guaranteed. Notably, a new scientific approach was applied to formulate air pollution control policy during the APEC conference. In addition to the established measures, two periods of enhanced and targeted reduction measures were implemented according to the forecast in advance. Finally, suggestions for improving air quality in Beijing were offered on the basis of the monitoring results and analyses during the APEC conference.

Federal Air-Quality Standards and Their Implementation

1976 ◽  
Vol 1 (2) ◽  
pp. 365-409 ◽  
Author(s):  
David P. Currie
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Motor Vehicle ◽  
Quality Standards ◽  
Air Pollutant ◽  
Federal State ◽  
Air Pollution Control ◽  
Air Quality Standards ◽  
Stationary Sources

With the enactment of the Clean Air Act amendments in 1970, the federal government, essentially an interested bystander as recently as 1960, became the dominant presence in air pollution control. The current statute provides for federal research, financial support of state control programs, and interstate compacts (secs. 101-106). It retains, in vestigial form, a cumbersome conference procedure (sec. 115) copied from the earlier water-pollution statute and never much used in air pollution. irect federal regulatory authority was substantially increased by the 1970 amendments: the federal Environmental Protection Agency (EPA) may now adopt emission standards not only for new vehicles (sec. 202) but also for all aircraft (sec. 231), for new stationary sources of any type that “may contribute significantly to air pollution” (sec. 111), and for any source of a “hazardous” air pollutant, that is, one that “may cause, or contribute to, an increase in mortality or … in serious irreversible, or incapacitating reversible, illness” (sec. 1 12); it may also regulate the contents of motor-vehicle fuels (sec. 211). An emergency provision authorizes the federal agency, absent adequate state or local action, to sue to enjoin any emissions contributing to “an imminent and substantial endangerment to the health of persons” (sec. 303). Yet everyday control of most existing stationary sources remains subject to an awkward joint federal-state system of air-quality standards. That system is the subject of the present article.

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 Assessments of Air Pollution Control Effectiveness Based on a Sharp Regression Discontinuity Design —Evidence From China’s Environmental Big Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Ren Wang ◽  
Jiaqi Huang ◽  
Lizhi Zhang ◽  
Yu Xia ◽  
Xu Xu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Air Quality ◽  
Pollution Control ◽  
Regression Discontinuity ◽  
Air Pollutant ◽  
Air Pollution Control ◽  
Short Term Treatment ◽  
Control Effectiveness ◽  
South Central

The Assessment andAppraisal Method for Ecological Construction Targets (the Method) was promulgated in 2016, which provided a concrete instruction for China’s air pollution control and established an explicit standard for reducing air pollutant concentration. This study implements a sharp regression discontinuity (RD) design and makes an assessment on air quality control effectiveness of the Method based on the high-volume big data acquired from 173 cities in China. The results show that the Method has significantly improved air pollution control on the overall air quality index (AQI) and reducing concentrations of PM2.5, PM10, SO2, NO2, and CO across the country in the observation periods. However, no reduction effect was observed for O3. The robustness tests support the conclusion as well. Besides, the heterogeneity analysis illustrates that the policy had a significant short-term treatment effect in East, South, Central, North, Northwest, Southwest, and Northeast China. However, the Method’s effect is found to decline over time either nationwide or regionally according to the persistence analysis. Therefore, this article puts forward several suggestions regarding the formulation of long-term regulations for air pollution control, the transformation of the growth model for sustainable development, and optimization of the incentive system for improved pollution control and prevention.

scholarly journals Evaluation of Multi-variate Time Series Clustering for Imputation of Air Pollution Data

2021 ◽  
Author(s):  
Wedad Alahamade ◽  
Iain Lake ◽  
Claire E. Reeves ◽  
Beatriz De La Iglesia
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Missing Data ◽  
Multivariate Time Series ◽  
Real Data ◽  
Economic Benefits ◽  
Air Pollutant ◽  
Monitoring Site ◽  
Concentration Data

Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a Multivariate Time Series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutants concentrations produced very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours, and of the characteristics of different stations according to their environmental type.

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