Skip to main content
Have a personal or library account? Click to login
Sensitivity assessment of PM2.5 simulation to the below-cloud washout schemes in an atmospheric chemical transport model Cover

Sensitivity assessment of PM2.5 simulation to the below-cloud washout schemes in an atmospheric chemical transport model

Open Access
|Jan 2018

Figures & Tables

Table 1.

Raindrop size distributions extracted from the literature.

ReferencesFormulaAMarshall and Palmer (1948) a ,1N0 = 8×106, α = 0, γ = 1,β = 4100P−0.21BUlbrich (1983) a ,1N0 = 5.1×106P−0.03, α = 0, γ = 1, β = 3800P−0.2CCerro et al. (1997) a ,1N0 = 2.32×106P0.22, α = 0, γ = 1, β = 4000P−0.1065DDe Wolf (2001) a ,1N0 = 1.22×10 1 6P−0.384, α = 2.93, γ = 1, β = 5380P−0.186EChapon et al. (2008) a ,1N0 = 7.5×10 1 8P0.001, α = 3.47, γ = 1, β = 7640P−0.214FChen et al. (2013) a ,2N0 = 2.1×104, α = 3.51, γ = 1, β = 4.52GAoki et al. (2016) a ,2N0 = 8.3×103, α = 2.3, γ = 1, β = 3.2HDas et al. (2017) a ,2N0 = 3.16×106, α = 7.9, γ = 1, β = 9.9IChen et al. (2017) a ,2N0 = 610, α = −0.158, γ = 1, β = 2.26JFeingold and Levin (1986) b Ntot = 1.72×10 2 P 0.22, Dmean = 7.2×10−4P−0.23, σD = 1.43–3×10−4PKTimothy et al. (2002) b Ntot = 2.03×10 2 P 0.241, Dmean = e−0.313 + 0.227ln P ×10−3, σD = e0.108-0.0086lnPLHarikumar et al. (2010) b Ntot = 2.68×10 2 P 0.308, Dmean = 5.96×10−4P−0.216, σD = 1.55–0.0217lnP

a Gamma distribution: N(D)=N0Dαexp(-βDγ). When α = 0, γ = 1, the equation then becomes the exponential distribution.

1 N 0 unit is in m−3 m−1-α, β unit is in m−γ.

2 N 0 unit is in m−3  mm−1-α, β unit is in mm−γ.

b Log-normal distribution: N(D)=Ntot2πDln(σD)exp[-(ln(D)-ln(Dmean))22(ln(σD))2]. Ntot is in m−3, Dmean is in m.

Fig. 1.

(a–c): WRF (dash line) and CAMx (solid line) domain setting. Blue dots and red numbers represent the locations of the ground-based PM2.5 observation station and precipitation observation station, respectively. ‘SZ’ represents Shenzhen, ‘HK’ represents Hong Kong, ‘HZ’ represents Huizhou, ‘DG’ represents Dongguan, ‘ZS’ represents Zhongshan, ‘ZH’ represents Zhuhai, ‘JM’ represents Jiangmen, ‘ZQ’ represents Zhaoqing, ‘GZ’ represents Guangzhou, and ‘FS’ represents Foshan.

Fig. 2.

(A–B): Simulated geographic distribution of accumulated precipitation mapping (by WRF) in the PRD region during September 3–13, 2010 (A). The numbers on the x-axis in subfigure B represent different precipitation observation stations. The geological locations of these stations can be found in Fig. 1c, marked by red numbers.

Table 2.

Model statistical metrics using different BCW schemes.

CaseSchemeMeanMBMENMBNMEACAMx v6.0026.54.117.00.200.77BCAMx v6.4018.9−3.514.9−0.150.67CLoosmore et al. (2004)25.32.916.90.150.76DAURAMS25.22.816.40.140.74EEMEP24.11.716.00.090.72FCMAQ11.9−10.514.9−0.470.67GCALPUFF16.0−6.414.9−0.280.67HADMS16.6−5.814.6−0.260.65ISparmacher et al. (1993)26.03.616.80.180.76JLaakso et al. (2003)23.51.115.60.060.70KWang et al. (2014c)20.2−2.214.3−0.090.64LBaklanov and Sorensen. (2001)16.9−5.514.7−0.240.66MKang et al. (2015)26.54.117.00.200.77NWang et al. (2014a)26.33.916.90.190.75Observations22.4

[i] ‘Mean’ represents the mean PM2.5 concentration over the 10 observation stations in the study period; ‘MB’ represents the mean bias; ‘ME’ represents the mean error; ‘NMB’ represents the normalized mean bias; ‘NME’ represents the normalized mean error. The lowest values for MB, ME, NMB and NME are bolded.

Fig. 3.

Time series of PM2.5 simulation using different BCW schemes at Tung Chung (a), Tai Po (b), Tsuen Wan (c), and Wanqingsha (d) stations.

Fig. 4.

Spatial difference (compared to CAMx v6.40) of PM2.5 simulation by using different BCW schemes (Unit: %). Letter labels on the panels refer to the simulations described in Table 2.

Table 3.

PM2.5 average simulated concentration (μg m−3) by different BCW schemes for each city during the study period.

City*ABCDEFGHIJKLMNHZ20.112.719.418.817.96.910.310.719.718.313.811.020.120.1GZ40.230.238.738.636.520.226.126.839.437.431.327.240.140.1FS40.633.539.839.338.324.630.130.640.138.734.131.040.640.6DG44.335.142.942.841.124.529.530.643.641.636.030.944.244.2JM21.417.120.920.220.211.515.715.821.120.417.316.121.321.4SZ35.827.134.434.532.818.422.723.735.333.528.623.935.835.8ZS30.924.329.929.628.817.121.522.030.429.125.022.330.830.8ZQ27.221.426.825.925.814.519.719.926.926.021.920.327.227.2HK22.115.020.920.919.88.812.513.021.720.316.313.322.122.1ZH23.217.522.322.021.412.115.816.122.821.918.316.323.123.2

* The cities are identified in Fig. 1.

Table 2 describes the simulations A-N and Fig. 1 defines the city acronyms and shows their locations.

Fig. 5. Time series of PM2.5 simulation using different raindrop size distribution parameterizations at Tung Chung (a), Tai Po (b), Tsuen Wan (c) and Wanqingsha (d) stations.

Fig. 6.

Spatial difference (compared to CAMx v6.40) of PM2.5 simulation by using different raindrop size distribution parameterizations (Unit: %). Letter labels on the panels refer to the simulations described in Table 1.

Table 4.

Source contribution (in %) to the sum of three PM2.5 components (zelb_a_1476435_ilg0001.gif, zelb_a_1476435_ilg0002.gif and zelb_a_1476435_ilg0003.gif).

CAMx v6.40CAMx v6.00EMEPCityLocalRegionalS-regionalLocalRegionalS-regionalLocalRegionalS-regionalHZ23.019.657.417.316.466.418.416.764.9GZ30.217.852.026.716.057.427.416.356.3FS29.319.151.526.317.156.626.917.555.6DG23.933.043.118.628.852.619.829.850.4JM21.28.670.219.57.772.819.87.872.3SZ34.526.339.226.922.850.328.723.547.8ZS20.125.854.118.222.059.818.722.858.6ZQ18.915.465.717.613.768.717.813.868.3HK18.433.048.615.124.460.516.026.257.8ZH16.321.362.414.317.268.514.918.267.0

[i] Figure 1 defines the city acronyms and shows their locations.

Table 5.

The BCW coefficients calculated by field observation data at the HKUST super-site.*

Time (DD_HHMM)zelb_a_1476435_ilg0003.gif (s−1)zelb_a_1476435_ilg0002.gif (s−1)zelb_a_1476435_ilg0001.gif (s−1)Precip (mm)Duration (min)6.06_6:00-7:591.9 × 10−43.4 × 10−48.7 × 10−516.8216.08_1:00-5:594.5 × 10−43.2 × 10−42.7 × 10−435.0726.10_18:00-18:593.6 × 10−43.2 × 10−42.5 × 10−410.9266.10_21:00-21:595.9 × 10−45.6 × 10−44.0 × 10−45.6156.11_11:00-12:59−5.1 × 10−52.4 × 10−41.2 × 10−412.4436.12_7:00-8:59−1.1 × 10−41.9 × 10−45.1 × 10−510.7326.12_10:00-10:592.4 × 10−4−1.7 × 10−43.9 × 10−44.3156.17_4:00-4:592.9 × 10−47.7 × 10−41.4 × 10−45.8136.17_7:00-7:592.0 × 10−42.8 × 10−41.7 × 10−48.4177.09_22:00-22:59−3.0 × 10−54.7 × 10−4−9.5 × 10−510.4167.11_3:00-3:592.7 × 10−43.9 × 10−41.9 × 10−44.3127.11_6:00-6:596.9 × 10−52.2 × 10−49.0 × 10−511.9217.13_7:00-7:592.4 × 10−58.3 × 10−52.2 × 10−515.5417.14_12:00-12:591.6 × 10−42.7 × 10−42.8 × 10−54.1138.10_7:00-7:592.7 × 10−45.0 × 10−43.3 × 10−439.6608.11_9:00-11:597.1 × 10−51.5 × 10−44.7 × 10−514.2458.28_1:00-2:596.4 × 10−43.4 × 10−48.8 × 10−414.535Median2.0 × 10−43.2 × 10−41.4 × 10−4––

* The HKUST super-site is in Clear Water Bay, Kowloon, Hong Kong.

Language: English
Page range: 1476435 - 1476435
Submitted on: Oct 9, 2017
Accepted on: May 8, 2018
Published on: Jan 1, 2018
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2018 Xingcheng Lu, Jimmy C. H. Fung, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.