Forecasting Hourly Roadside Particulate Matter in Taipei County of Taiwan Based on First‐Order and One‐Variable Grey Model

Pai, Tzu‐Yi; Hanaki, Keisuke; Chiou, Ren-Jie

doi:10.1002/clen.201000402

Cited by 33 publications

(22 citation statements)

References 27 publications

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“…Furthermore, the research on the mechanisms of air pollution is still in a discovery phase. In 2003, Dr. Pai and his colleges adopted grey system method on air pollution study, and confirmed it effectiveness [8]. Thus, we used the grey relational analysis to study the nonlinear multiple-dimensional model of the social economic activities and the impacts on air pollution.…”

Section: Resultsmentioning

confidence: 85%

“…Pai et al [8] confirmed the effectiveness of adopting the grey system method to study air pollution problems and their main determinants. However, they utilized grey analysis method on forecasting air pollution, but failed to explore the causes of pollution [8]. Thus, in this study, we focus on the most serious air polluted region, the capital of China, and explore the relationship between the socio-economic development and the air quality in Beijing.…”

Section: Introductionmentioning

confidence: 95%

“…The Air pollution is complex and separating the haze components has proved complex [7]. Pai et al [8] confirmed the effectiveness of adopting the grey system method to study air pollution problems and their main determinants. However, they utilized grey analysis method on forecasting air pollution, but failed to explore the causes of pollution [8].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Social-economic Activities on Air Pollutants in Beijing, China

Zheng

Yin

et al. 2017

Open Geosciences

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Abstract:With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM 2.5 ), particulate matter with size 10 micrometers or less (PM 10 ), sulfur dioxide (SO 2 ) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM 2.5 , while PM 10 , SO 2 , and NOx present in general moderate correlations with most of the socioeconomic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have

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Section: Resultsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Influence of Social-economic Activities on Air Pollutants in Beijing, China

Zheng

Yin

et al. 2017

Open Geosciences

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“…The forecasting of air quality became popular, many methods have been purposed for forecasting air quality such as hidden Markov model [15], first-order and one-variable grey model [16], developed support vector machine [17], fuzzy time series model [18][19] [25], Solar Radiation [26], and Fuzzy-AHP [27].…”

Section: Related Workmentioning

confidence: 99%

Web Based Information System of Carbon Monoxide Pollution

2018

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In Indonesia, carbon monoxide is one of which type of gas used as a parameter in the air pollution. Unfortunately, reporting and monitoring air pollution in Indonesia is regulated in government rules and reported once a day. The value of carbon monoxide concentration always change but the published information is out of date. Without real-time information, people cannot avoid the danger of monoxide pollution toxicity effect. This paper purpose the solution by publishes the real-time information from the carbon monoxide sensor data acquisition. This Research uses the rule-based method to calculate carbon monoxide pollution from data acquisition. Results of experiments show that information generated by the system in accordance with the manual calculation results of carbon monoxide pollution. This research contributed to providing information on real-time carbon monoxide measurement to the public, so the danger of carbon monoxide poisoning can be avoided.

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“…However, many researchers have been focusing on these types of forecasts [4][5][6][7][8]. The most common forecasting approaches are numerical models and statistical models.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

et al. 2015

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Abstract:In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO 2 , NO 2 , and PM 10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO 2 , NO 2 , and PM 10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM 10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R 2 values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale. OPEN ACCESSAtmosphere 2015, 6 892

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Forecasting Hourly Roadside Particulate Matter in Taipei County of Taiwan Based on First‐Order and One‐Variable Grey Model

Cited by 33 publications

References 27 publications

Influence of Social-economic Activities on Air Pollutants in Beijing, China

Influence of Social-economic Activities on Air Pollutants in Beijing, China

Web Based Information System of Carbon Monoxide Pollution

Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

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