Chongqing Air Quality Characteristics and Relationship with Meteorological Elements

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.

Analysis on Variation Characteristics of Air Pollution Index in Cangzhou

2013 ◽  
Vol 448-453 ◽  
pp. 349-352
Author(s):  
Jian Wang ◽  
Mei Xu ◽  
Xia Ye ◽  
Wei Liu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Index ◽  
Weather Conditions ◽  
Local Source ◽  
Quality Level ◽  
Air Mass ◽  
Air Pollution Index ◽  
Adverse Weather ◽  
Variation Characteristics

On the basis of the daily air pollution index (API), primary pollutant, air quality level and status in Cangzhou from January 2009 to December 2012, the variation characteristics of air quality were analyzed. The results showed that API presented obvious seasonal variations with the highest value in winter and the lowest value in summer. PM10was the principal pollutant and accounted for 63% of the whole period. The percentage of excellent and good air quality was 93% during the period. The relationship between API and meteorological factors indicated that API was negatively correlated with temperature and relative humidity, but was not obvious correlation with air pressure and wind speed. The cluster analysis of backward trajectories showed that different types of air masses contribution to the API level showed some differences in different seasons. The north air mass in fall and northwest air mass in summer had the lowest contribution to the API level; while the local source could lead to the highest API level in winter suggesting that the heating and adverse weather conditions in winter may be the main cause of high API.

Forecasting of Air Pollution Index with Artificial Neural Network

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Nur Haizum Abd Rahman ◽  
Muhammad Hisyam Lee ◽  
Mohd Talib Latif ◽  
Suhartono S.
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Time Series ◽  
Artificial Neural Network ◽  
Air Quality ◽  
Warning System ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Air Pollution Index ◽  
Artificial Neural

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.

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

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.

scholarly journals A Parameterized Method for Air-Quality Diagnosis and Its Applications

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
J. Z. Wang ◽  
S. L. Gong ◽  
X. Y. Zhang ◽  
Y. Q. Yang ◽  
Q. Hou ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Velocity ◽  
Pollution Index ◽  
Weather Factors ◽  
Opening Ceremony ◽  
Higher Temperature ◽  
Sport Events ◽  
Meteorological Elements ◽  
Quality Diagnosis

A parameterized method is developed to diagnose the air quality in Beijing and other cities with an index termed (parameters linking air-quality to meteorological elements PLAM) derived from a correlation between PM10and relevant weather elements based on the data between 2000 and 2007. Key weather factors for diagnosing the air pollution intensity are identified and included in PLAM that include atmospheric condensation of water vapour, wet potential equivalent temperature, and wind velocity. It is found that the poor air quality days with elevated PM10are usually associated with higher PLAM values, featuring higher temperature, humidity, lower wind velocity, and higher stability compared to the averaged values in the same period. Both 24 h and 72 h forecasts provided useful services for the day of the opening ceremony of the Beijing Olympic Games and subsequent sport events. A correlation coefficient of 0.82 was achieved between the forecasts and (air pollution index API) and 0.59 between the forecasts and observed PM10, all reaching the significant level of 0.001, for the summer period. A correction factor was also introduced to enable the PLAM to diagnose the observed PM10concentrations all year round.

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

2011 ◽  
Vol 46 (12) ◽  
pp. 2562-2569 ◽  
Author(s):  
Wei-Zhen Lu ◽  
Hong-di He ◽  
Andrew Y.T. Leung
Keyword(s):  
Air Pollution ◽  
Hong Kong ◽  
Air Quality ◽  
Pollution Index ◽  
Air Pollution Index

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

2019 ◽  
Vol 11 (19) ◽  
pp. 5190 ◽  
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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