Assessing air quality in Hong Kong: A proposed, revised air pollution index (API)

In recent years, the arisen of air pollution in urban area address much attention globally. The air pollutants has emerged detrimental effects on health and living conditions. Time series forecasting is the important method nowadays with the ability to predict the future events. In this study, the forecasting is based on 10 years monthly data of Air Pollution Index (API) located in industrial and residential monitoring stations area in Malaysia. The autoregressive integrated moving average (ARIMA), fuzzy time series (FTS) and artificial neural network (ANNs) were used as the methods to forecast the API values. The performance of each method is compare using the root mean square error (RMSE). The result shows that the ANNs give the smallest forecasting error to forecast API compared to FTS and ARIMA. Therefore, the ANNs could be consider a reliable approach in early warning system to general public in understanding the air quality status that might effect their health and also in decision making processes for air quality control and management.

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Temporal Variation of Urban Air Quality in Nanchong, a Big City of Southwest China

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1010-1012.385 ◽

2014 ◽

Vol 1010-1012 ◽

pp. 385-388

Author(s):

Kun Li ◽

Xiao Shuang Tong ◽

You Ping Li ◽

Hong Zhou

Keyword(s):

Air Pollution ◽

Air Quality ◽

Temporal Variation ◽

Southwest China ◽

Pollution Index ◽

Urban Air Quality ◽

Urban Air ◽

Mean Values ◽

Air Pollution Index ◽

Big City

The article applied 2008-2012 hourly mass concentrations of PM10, SO2and NO2and air pollution index (API) data to discuss the temporal variation of urban air quality in Nanchong, a big southwest city in China. The results showed that the annual mean PM10,SO2and NO2concentrations during 5 years were 61±1μg.m-3, 45±4μg.m-3, 35±5μg.m-3, respectively. And the annual mean concentrations and API values presented decreasing tendency, which were less than the annual second-level air quality limit except for NO2in 2008. In addition, the monthly mean values in spring and winter were higher than those in summer and fall, which the maximum appeared in December, and January, the minimum appeared in July and August.

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Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

Sustainability ◽

10.3390/su11195190 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5190 ◽

Cited By ~ 4

Author(s):

Nurul Nnadiah Zakaria ◽

Mahmod Othman ◽

Rajalingam Sokkalingam ◽

Hanita Daud ◽

Lazim Abdullah ◽

...

Keyword(s):

Air Pollution ◽

Markov Chain ◽

Air Quality ◽

Markov Chain Model ◽

Model Development ◽

Pollution Index ◽

Pollution Level ◽

Chain Model ◽

Air Pollution Index ◽

Long Run

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

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Chongqing Air Quality Characteristics and Relationship with Meteorological Elements

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.726-731.1265 ◽

2013 ◽

Vol 726-731 ◽

pp. 1265-1268

Author(s):

Yu Han ◽

De Liu ◽

Chang Song Gai ◽

Fan Hua Min

Keyword(s):

Air Pollution ◽

Air Quality ◽

Sunshine Duration ◽

Phase Variation ◽

Pollution Index ◽

Air Pollution Index ◽

Level Ii ◽

Monthly Total ◽

Variation Characteristics ◽

Meteorological Elements

Analyze the air pollution index (API) variation characteristics in Chongqing, and the relationship between API and meteorological elements. Found that air quality of Chongqing is basically at level II. Primary pollutant is inhalable particle (PM10). Summer is the season with best air quality, while winter is the most polluted season. Visibility, precipitation, sunshine and other meteorological elements have a great effect on the variation of air quality. The correlation between air pollution index and visibility is the most significant, which is-0.37. However, inter-monthly variation of monthly total rainfall, monthly total sunshine duration and API shows obvious reverse phase variation.

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