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Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (5): 317-328.doi: 10.3724/SP.J.1226.2020.00317.

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Variation characteristics and prediction of pollutant concentration during winter in Lanzhou New District, China

DongYu Jia1,XiaoXia Li2(),XiaoQing Gao3,LiWei Yang3   

  1. 1.College of Geography and Environmental Engineering, Lanzhou City University, Lanzhou, Gansu 730070, China
    2.Northwest Regional Climate Center/Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Arid Climatic Change and Disaster of China Meteorological Administration/Institute of Arid Meteorology, Lanzhou, Gansu 730020, China
    3.Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions/Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
  • Received:2020-04-01 Accepted:2020-10-01 Online:2020-10-31 Published:2020-10-29
  • Contact: XiaoXia Li E-mail:554197933@qq.com

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Abstract:

PM2.5 and PM10 were the main air pollutants during winter in Lanzhou New District, China. In this paper, WRF model output combined with hourly monitoring data of pollutant concentration was used to analyze characteristics of the concentration change and to study the relationship between meteorological elements and PM10/PM2.5 in Lanzhou New District in January, 2018. Meanwhile, the concentration changes of PM2.5 and PM10 were predicted by wavelet analysis combined with BP neural network. The results show that: (1) Due to the cold front process in winter, PM2.5 was negatively correlated with the water vapor mixing rate. PM10 was positively correlated with air temperature and negatively correlated with air pressure. (2) There was an inversion layer in the atmosphere near the high value day of PM2.5 and PM10, the surface was controlled by low pressure, low wind speed, and the situation of low value day of PM2.5 was the opposite. On the day of high value of PM10, the air temperature below 600 hPa was higher, and the wind speed near the surface was also higher. (3) Wavelet analysis combined with BP (Back Propagation) neural network had a good prediction effect on PM2.5, which could basically reflect the hourly change of PM2.5 concentration. However, the simulation effect of PM10 was poor, and the input parameters of surrounding pollutants should be added to improve the prediction effect.

Key words: PM2.5, PM10, WRF, Wavelet neural network, Lanzhou New District

Table 1

Geographical location of observation point"

Observation pointGeographic positionAltitude (a.s.l.)
Observation point 136.61 °N, 103.76 °E2,047 m
Observation point 236.65 °N, 103.56 °E2,099 m
Observation point 336.46 °N, 103.70 °E1,977 m

Observation point 4

Observation point 5

36.53 °N, 103.66 °E

36.47 °N, 103.70 °E

1,963 m

1,946 m

Figure 1

Three-layer nested domain of WRF (The black points are the observation station locations)"

Table 2

The parameter setting in WRF"

Parameterized schemeParameter settings
Microphysical parameterization scheme WSM3WSM3 (Single-Moment 3-class Microphysics scheme)
Long wave radiation program RRTMRRTM (Rapid radiative transfer model)
Short wave radiation schemeDudhia

Boundary layer scheme

Cumulus convection parameter scheme

Land surface process scheme

Urban canopy scheme

MYJ (Mellor-Yamada-Janjic)

Kain-Fritsch

Noah

BEM (Building Energy Model)

Figure 2

The forecasting process of pollutant concentration by wavelet transform and neural network"

Figure 3

The temperature change of Lanzhou New District in January, 2018"

Figure 4

Daily average concentration change of PM10 and PM2.5 in January, 2018"

Figure 5

Days distribution of PM10 (a) and PM2.5 (b) at different concentration limits in January, 2018"

Table 3

Correlation between pollutant concentration and different meteorological elements"

Meteorological elementsObservation point 4Observation point 5
PM2.5PM10PM2.5PM10
Wind speed at 10 m0.24990.06650.02950.0685

Precipitation

Air temperature at 2 m

Air pressure at 2 m

Water vapor mixing rate

0.1587

0.2597

-0.4773

0.3457

0.0720

0.4637

-0.5011

0.3296

-0.2119

0.3251

-0.3518

0.1536

-0.0272

0.5533

-0.5066

0.3141

Figure 6

Concentration change of PM2.5 and boundary layer height on heavy (a, January 5; c, January 28) and light pollution days (b, January 5; d, January 28)"

Figure 7

Weather characteristics at 14:00 on the day with the highest concentration (a, b, c, January 5) and the lowest concentration (d, e, f, January 28) of PM2.5 (Figures a, d surface temperature and wind speed, Figures b, e near formation water vapor mixing rate, Figures c, f wind speed and temperature profile wind profile: black, temperature profile: blue)"

Figure 8

Weather characteristics at 14:00 on the day with the highest concentration (a, b, c, January 13) and the lowest concentration (d, e, f, January 20) of PM10 (Figures a, d surface temperature and wind speed, Figures b, e near formation water vapor mixing rate, Figures c, f wind speed and temperature profile wind profile: black, temperature profile: blue)"

Figure 9

Concentration change of PM10 and boundary layer height on heavy (a, January 13; b, January 20) and light pollution days (c, January 13; d, January 20)"

Figure 10

Prediction renderings of PM2.5 (a) and PM10 (b) at different observation points"

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