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Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality

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Abstract

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-to-day 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.

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Reference

  • Abderrahim, H., Chellali, M. R., & Hamou, A. (2016). Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks. Environmental Science and Pollution Research, 23, 1634–1641.

    Article  CAS  Google Scholar 

  • An, X. Q., Zuo, H. C., & Chen, L. J. (2007). Atmospheric environment capacity of SO2 in winter over Lanzhou in China: a case study. Advances in Atmospheric Sciences, 24, 688–699.

    Article  CAS  Google Scholar 

  • Asaf, D., Peleg, M., Alsawair, J., Soleiman, A., Matveev, V., Tas, E., Gertler, A., & Luria, M. (2011). Trans-boundary transport of ozone from the Eastern Mediterranean Coast. Atmospheric Environment, 45, 5595–5601.

    Article  CAS  Google Scholar 

  • Bandyopadhyay, G., & Chattopadhyay, S. (2007). Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. International Journal of Environmental Science and Technology, 4, 141–149.

    Article  CAS  Google Scholar 

  • Cai, M., Yin, Y., & Xie, M. (2009). Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transportation Research D-Tr E, 14, 32–41.

    Article  Google Scholar 

  • Chen, L. H., Yu, Y., Chen, J. B., Li, W. Y., & Li, J. L. (2010). Characteristics of main air pollution in Lanzhou during 2001–2007. Plateau Meteorology, 29(6), 1627–1633 (in Chinese).

  • Cheng, C. S. Q., Campbell, M., Li, Q., Li, G. L., Auld, H., Day, N., Pengelly, D., Gingrich, S., & Yap, D. (2007). A synoptic climatological approach to assess climatic impact on air quality in South-central Canada. Part I: Historical analysis. Water Air Soil Pollution, 182, 131–148.

    Article  CAS  Google Scholar 

  • Fernando, H. J. S., Mammarella, M. C., Grandoni, G., Fedele, P., Marco, R. D., Dimitrova, R., & Hyde, P. (2012). Forecasting PM10 in metropolitan areas: efficacy of neural networks. Environmental Pollution, 163, 62–67.

    Article  CAS  Google Scholar 

  • Grivas, G., & Chaloulakou, A. (2006). Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment, 40, 1216–1229.

    Article  CAS  Google Scholar 

  • Guo, Q. C., He, Z. F., Kou, L. Q., Li, L., Zhang, X. Y., & Kong, L. J. (2011). Application of BP neural network to forecasting API in Beijing. Environmental Engineering, 29, 106–108 (in Chinese).

    Google Scholar 

  • He, J. J., Wu, L., Mao, H. J., Liu, H. L., Jing, B. Y., Yu, Y., Ren, P. P., Feng, C., & Liu, X. H. (2016b). Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing—Part 2: impact of vehicle emission on urban air quality. Atmospheric Chemistry and Physics, 16, 3171–3184.

    Article  CAS  Google Scholar 

  • He, J. J., Yu, Y., Chen, J. B., Liu, N., & Zhao, S. P. (2012). Simulation study of the influence of vegetation fraction on meteorological condition in Lanzhou using WRF model. Plateau Meteorology, 31, 1161–1621 (in Chinese).

    Google Scholar 

  • He, J. J., Yu, Y., Liu, N., Zhao, S. P., & Chen, J. B. (2014). Impact of land surface information on WRF’s performance in complex terrain area. Chinses Journal of Atmospheric Science, 38, 484–498 (in Chinese).

    Google Scholar 

  • He, J. J., Yu, Y., Liu, N., & Zhao, S. P. (2013). Numerical model-based relationship between meteorological conditions and air quality and its implication for urban air quality management. International Journal of Environment and Pollution, 53, 265–286.

    Article  CAS  Google Scholar 

  • He, J. J., Wu, L., Mao, H. J., Li, R. P. (2016a). Impact of meteorological condition on air quality in urban Langfang, Hebei Province. Research of Environmental Sciences, accepted (in Chinese).

  • Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., & Brasseur, O. (2005). A neural network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment, 39, 3279–3289.

    Article  CAS  Google Scholar 

  • Hu, Y. Q., & Zhang, Q. (1999). Atmosphere pollution mechanism along with prevention and cure countermeasure of the Lanzhou hollow basin. China Environmental Science, 19(2), 119–122 (in Chinese).

  • Huang, R. J., Zhang, Y. L., Bozzetti, C., Ho, K. F., Cao, J. J., Han, Y. M., Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S. M., Bruns, E. A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z. S., Szidat, S., Baltensperger, U., Haddad, I. E., & Prévôt, A. S. H. (2014). High secondary aerosol contribution to particulate pollution during haze events in China. Nature, 514, 218–222.

    CAS  Google Scholar 

  • Huth, R. (1996). An intercomparison of computer-assisted circulations classification methods. International Journal of Climatology, 16, 893–922.

    Article  Google Scholar 

  • Jia, X. P., & Chen, K. F. (2011). Impact of dust events on air PM10 concentration in the Lanzhou Valley. Journal of Desert Research, 31, 1573–1578 (in Chinese).

    Google Scholar 

  • Jiang, D. P., Wang, S. G., Lang, X. M., Shang, K. Z., & Yang, D. B. (2001). The characteristics of stratification of lower-layer atmospheric temperature and their relations with air pollution in Lanzhou proper. Journal of Lanzhou University, 37(4), 133–139 (in Chinese).

    Google Scholar 

  • Jiang, N. B., Betts, A., & Riley, M. (2016). Summarising climate and air quality (ozone) data on self-organising maps: a Sydney case study. Environmental Monitoring and Assessment, 188, 103.

    Article  Google Scholar 

  • Jiang, N. B., Dirks, K. N., & Luo, K. H. (2014). Effects of local, synoptic and large-scale climate conditions on daily nitrogen dioxide concentrations in Auckland, New Zealand. International Journal of Climatology, 34, 1883–1897.

    Article  CAS  Google Scholar 

  • Jimenez, P., Jorba, O., Parra, R., & Baldasano, J. M. (2005). Influence of high-model grid resolution on photochemical modelling in very complex terrains. International Journal of Environment and Pollution, 24, 180–200.

    Article  CAS  Google Scholar 

  • Kioutsioukis, I., de Meij, A., Jakobs, H., Katragkou, E., Vinuesa, J., & Kazantzidis, A. (2016). High resolution WRF ensemble forecasting for irrigation: multi-variable evaluation. Atmospheric Research, 167, 156–174.

    Article  Google Scholar 

  • Lee, C. C., Ballinger, T. J., & Domino, N. A. (2012). Utilizing map pattern classification and surface weather typing to relate climate to the air quality index in Cleveland, Ohio. Atmospheric Environment, 63, 50–59.

    Article  CAS  Google Scholar 

  • Liu, D., & Gao, S. C. (2011). Determining the number of clusters in K-means clustering algorithm. Silicon Valley, 6, 38–39 (in Chinese).

    Google Scholar 

  • Liu, J., Yang, P., Lü, W. S., Liu, A., & Liu, J. X. (2015). Prediction models of PM2.5 mass concentration based on meteorological factors. Journal of Shangdong University, 45, 76–83 (in Chinese).

    CAS  Google Scholar 

  • Liu, N., Yu, Y., He, J. J., & Zhao, S. P. (2013). Integrated modeling of urban-scale pollutant transport: application in a semi-arid urban valley, Northwestern China. Atmosphere Pollution Research, 4, 306–314.

    Article  CAS  Google Scholar 

  • Oanh, N. T. K., Chutimon, P., Ekbordin, W., & Supat, W. (2005). Meteorological pattern classification and application for forecasting air pollution episode potential in a mountain-valley area. Atmospheric Environment, 39, 1211–1225.

    Article  CAS  Google Scholar 

  • Pearce, J. L., Beringer, J., Nicholls, N., Hyndman, R. J., Uotila, P., & Tapper, N. J. (2011a). Investigating the influence of synoptic-scale meteorology on air quality using self-organizing maps and generalized additive modeling. Atmospheric Environment, 45, 128–136.

    Article  CAS  Google Scholar 

  • Pearce, J. L., Beringer, J., Nicholls, N., Hyndman, R. J., & Tapper, N. J. (2011b). Quantifying the influence of local meteorology on air quality using generalized additive models. Atmospheric Environment, 45, 1328–1336.

    Article  CAS  Google Scholar 

  • Qi, J. H., Li, P. L., Li, X. G., Feng, L. J., & Zhang, M. P. (2006). Estimation of dry deposition fluxes of aerosol particles in the Qingdao area II: dry deposition fluxes of six metal elements. Periodical of Ocean University of China, 36(3), 461–467 (in Chinese).

    CAS  Google Scholar 

  • Qu, W. J., Arimoto, R., Zhang, X. Y., Zhao, C. H., Wang, Y. Q., Sheng, L. F., & Fu, G. (2010). Spatial distribution and interannual variation of surface PM10 concentrations over eighty-six Chinese cities. Atmospheric Chemistry and Physics, 10,5641–5662.

  • Shang, K. Z., Da, C. Y., Fu, Y. Z., Wang, S. G., & Yang, D. B. (2001). The stable energy in Lanzhou city and the relations between pollution and it. Plateau Meteorology, 20, 76–81 (in Chinese).

    Google Scholar 

  • Skamarock, W. C., & Klemp, J. B. (2008). A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. Journal of Computational Physics, 227, 3465–3485.

    Article  Google Scholar 

  • Stoeckenius, T. E., Hogrefe, C., Zagunis, J., Sturtz, T. M., Wells, B., & Sakulyanontvittaya, T. (2015). A comparison between 2010 and 2006 air quality and meteorological conditions, and emissions and boundary conditions used in simulations of the AQMEII-2 North American domain. Atmospheric Environment, 115, 389–403.

    Article  CAS  Google Scholar 

  • Tian, H. W., Tan, J. G., & Du, Z. X. (2008). Study of Shanghai ambient air quality with temporal synoptic index. Meteorological and Environmental Sciences, 31, 51–55 (in Chinese).

    Google Scholar 

  • Wang, X., & An, X. Q. (2007). The analysis of pollutant emission and energy situation in Lanzhou. Gansu Science and Technology, 22, 5–8 (in Chinese).

    Google Scholar 

  • Willmott, C. J. (1981). On the validation of models. Physical Geography, 2, 184–94.

    Google Scholar 

  • Xu, H., Zhang, H., Xing, Z. Y., & Deng, J. J. (2015). Pollution characteristics and ozone formation potential of ambient VOCs in winter and spring in Xiamen. Environmental Science, 36, 11–17 (in Chinese).

    Google Scholar 

  • Yu, W. G., Wang, T. J., Yang, C., & Sun, Y. (2008). Application of PCA-BP neural network to SO2 concentration forecast. Meteorological Monthly, 34, 97–101 (in Chinese).

    Google Scholar 

  • Zhang, J. P., Zhu, T., Zhang, Q. H., Li, C. C., Shu, H. L., Ying, Y., Dai, Z. P., Wang, X., Liu, X. Y., Liang, A. M., Shen, H. X., & Yi, B. Q. (2012). The impact of circulation patterns on regional transport pathways and air quality over Beijing and its surrounding. Atmospheric Chemistry and Physics, 12, 5031–5051.

    Article  CAS  Google Scholar 

  • Zhang, L., Liu, L. C., Zhao, Y. H., Gong, S. L., Zhang, X. Y., Henze, D. K., Capps, S. L., Fu, T. M., Zhang, Q., & Wang, Y. X. (2015). Source attribution of particulate matter pollution over North China with the adjoint method. Environmental Research Letters, 10, 084011.

    Article  Google Scholar 

  • Zhao, S. P., Yu, Y., He, J. J., Yin, D. Y., & Wang, B. (2015). Below-cloud scavenging of aerosol particles by precipitation in a typical valley city, northwestern China. Atmospheric Environment, 102, 70–78.

    Article  CAS  Google Scholar 

  • Zhao, Y. X., & Hou, Q. (2008). An analysis on spatial/temporal evolution of acid rain in China (1993–2006) and its causes. Acta Meteorologica Sinica, 66, 1032–1042 (in Chinese).

    Google Scholar 

  • Zhang, Q. (2001). The influence of terrain and inversion layer on pollutant transfer over Lanzhou City. China Environmental Science, 21(3), 230–234 (in Chinese).

  • Zheng, G. J., Duan, F. K., Su, H., Ma, Y. L., Cheng, Y., Zheng, B., Zhang, Q., Huang, T., Kimoto, T., Chang, D., Poschl, U., Cheng, Y. F., & He, K. B. (2015). Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions. Atmospheric Chemistry and Physics, 15, 2969–2983.

    Article  CAS  Google Scholar 

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Acknowledgments

This work was supported by the Chinese Academy of Sciences through the ‘100 Talent Project’ (No. 290827631), Opening Research Foundation of Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences (LPCC201405) and Lanzhou city Science and Technology Plan (No. 2009KJLQ).

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Authors and Affiliations

  1. Chinese Academy of Sciences, Cold & Arid Regions Environmental & Engineering Research Institute, Lanzhou, 730000, China

    Jianjun He, Ye Yu & Suping Zhao

  2. The College of Environmental Science & Engineering, Nankai University, Tianjin, 300071, China

    Jianjun He, Hongjun Mao & Lin Wu

  3. The College of Juvenile Class, University of Science and Technology of China, Hefei, 230026, China

    Yaochen Xie

  4. Weather Modification Office, Qinghai Meteorological Bureau, Xining, 810001, China

    Na Liu

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  1. Jianjun He
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  2. Ye Yu
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  5. Lin Wu
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  6. Na Liu
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  7. Suping Zhao
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Correspondence to Ye Yu.

Appendix

Appendix

1.1 Variable symbols

T 2: 2-m temperature

RH2: 2-m relative humidity

WS10: 10-m wind speed

P: surface pressure

PREC: precipitation

PR: precipitation rate

SH: sunshine hours

T: potential temperature

Q: water vapor mixing ratio

W s : wind speed

W di: wind direction index

E w: stable energy

F r: Froude number

H pbl: boundary layer height

γ: potential temperature lapse rate

l: transport index

R i: gradient Richardson number

IOA: index of agreement

R: correlation coefficient

RMSE: root mean square error

MB: mean bias

ME: mean error

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He, J., Yu, Y., Xie, Y. et al. Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality. Water Air Soil Pollut 227, 235 (2016). https://doi.org/10.1007/s11270-016-2930-z

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