Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality
文献类型：期刊论文
| 作者 | He, Jianjun1,2; Yu, Ye1; Xie, Yaochen3; Mao, Hongjun2; Wu, Lin2; Liu, Na4; Zhao, Suping1 |
| 刊名 | WATER AIR AND SOIL POLLUTION
 |
| 出版日期 | 2016-07-01 |
| 卷号 | 227期号:7页码:16 |
| 关键词 | Air quality Synoptic-scale circulation Local meteorology Pollutant emission Removal process ANN |
| ISSN号 | 0049-6979 |
| DOI | 10.1007/s11270-016-2930-z |
| 通讯作者 | Yu, Ye(yyu@lzb.ac.cn) |
| 英文摘要 | Knowledge of the relationship between air quality and impact factors is very important for air pollution control and urban environment management. Relationships between winter air pollutant concentrations and local meteorological parameters, synoptic-scale circulations and precipitation were investigated based on observed pollutant concentrations, high-resolution meteorological data from the Weather Research and Forecast model and gridded reanalysis data. Artificial neural network (ANN) model was developed using a combination of numerical model derived meteorological variables and variables indicating emission and circulation type variations for estimating daily SO2, NO2, and PM10 concentrations over urban Lanzhou, Northwestern China. Results indicated that the developed ANN model can satisfactorily reproduce the pollution level and their day-to-day variations, with correlation coefficients between the modeled and the observed daily SO2, NO2, and PM10 ranging from 0.71 to 0.83. The effect of four factors, i.e., synoptic-scale circulation type, local meteorological condition, pollutant emission variation, and wet removal process, on the day-to-day variations of SO2, NO2, and PM10 was quantified for winters of 2002-2007. Overall, local meteorological condition is the main factor causing the day-today variations of pollutant concentrations, followed by synoptic-scale circulation type, emission variation, and wet removal process. With limited data, this work provides a simple and effective method to identify the main factors causing air pollution, which could be widely used in other urban areas and regions for urban planning or air quality management purposes. |
| 收录类别 | SCI |
| WOS关键词 | SELF-ORGANIZING MAPS ; METEOROLOGICAL CONDITIONS ; PATTERN-CLASSIFICATION ; PM10 CONCENTRATIONS ; REGIONAL TRANSPORT ; FORECASTING PM10 ; RESOLUTION ; POLLUTION ; WEATHER ; CLIMATE |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences ; Water Resources |
| WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000379240400017 |
| 出版者 | SPRINGER |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2557223 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Yu, Ye |
| 作者单位 | 1.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China 2.Nankai Univ, Coll Environm Sci & Engn, Tianjin 300071, Peoples R China 3.Univ Sci & Technol China, Coll Juvenile Class, Hefei 230026, Peoples R China 4.Qinghai Meteorol Bur, Weather Modificat Off, Xining 810001, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Jianjun,Yu, Ye,Xie, Yaochen,et al. Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality[J]. WATER AIR AND SOIL POLLUTION,2016,227(7):16. |
| APA | He, Jianjun.,Yu, Ye.,Xie, Yaochen.,Mao, Hongjun.,Wu, Lin.,...&Zhao, Suping.(2016).Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality.WATER AIR AND SOIL POLLUTION,227(7),16. |
| MLA | He, Jianjun,et al."Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality".WATER AIR AND SOIL POLLUTION 227.7(2016):16. |
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来源：寒区旱区环境与工程研究所
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