Long-term trends and characteristics of visibility in two megacities in southwest China: Chengdu and Chongqing

Figures & data

Figure 1. (a) Spatial distribution of 30-yr average visibility in China (Sichuan and Chongqing are the regions within the gray lines). (b) Location and elevation of the Sichuan Basin, Chengdu and Chongqing (color figure available online).

Table 1. Air quality category and the upper limit of daily average concentrations for each pollutant corresponding to different API classifications

			Upper limit of daily average concentration (μg m^−3)
API		Air quality category	PM10	SO2	NO2
0–50	I	Clean	50	50	80
51–100	II	Good	150	150	120
101–200	III	Low-level pollution	350	800	280
201–300	IV	Mid-level pollution	420	1600	565
301–500	V	High-level pollution	600	2100	940

Table 2a. Summary of visibility, meteorological factors, API, and PM₁₀ pollution in Chengdu

Chengdu	Visibility (km)	T (K)	RH (%)	WS (m/s)	Pressure (hPa)	API	PM10 (μg m^−3)
Mean	8.2	289.8	78.6	1.3	1015.6	82	116.4
Median	8.0	290.6	78.0	1.2	1015.5	80	112.0
Maximum	36.2	307.2	100	7.0	1042.7	500	>600
Minimum	0.2	271.1	28.8	0.0	991.3	18	20.0
SD	3.9	7.4	10.0	0.74	9.5	30.2	54.4
N	13,844	13,844	13,844	1,3844	11,285	3860	3820
Notes: T: temperature; RH: relative humidity; WS: wind speed; SD: standard deviation; N: number of data samples.

Figure 2. Cumulative distribution functions of visibility in Chengdu and Chongqing.

Figure 3. Percentage occurrence of days with PM₁₀ as the primary pollutant and trends of PM₁₀ concentrations in Chengdu and Chongqing.

Figure 4. Monthly variations of visibility, PM₁₀, and meteorological factors in Chengdu (a, b) and Chongqing (c, d).

Figure 5. Trends of the 50th cumulative percentile of visibility and ridit values in (a) Chengdu and (b) Chongqing.

Figure 6. Days with visibility >19 km per year and days with visibility <10 km per year in Chengdu and Chongqing.

Figure 7. (a) Comparison of visibility between 1960s and 2000s in Chengdu and Chongqing. (b) Comparison of haze days between 1960s and 2000s in Chengdu and Chongqing.

Figure 8. Trends in GDP and growth of vehicle ownership in Chengdu and Chongqing.

Table 2b. Summary of visibility, meteorological factors, API, and PM₁₀ pollution in Chongqing

Chongqing	Visibility (km)	T (K)	RH (%)	WS m/s)	Pressure (hPa)	API	PM10 (μg m^−3)
Mean	6.2	291.3	78.5	1.5	1014.0	86	126.7
Median	5.1	291.4	80.0	1.5	1014.1	82	118.0
Maximum	37.5	309.0	99.7	6.7	1041.7	340	452.0
Minimum	0.5	273.1	27.4	0.0	989.9	11	14.0
SD	4.3	7.8	11.8	0.8	9.6	31.4	63.0
N	13,862	13,862	13,862	13,862	13,862	3569	3302
Notes: T: temperature; RH: relative humidity; WS: wind speed; SD: standard deviation; N: number of data samples.

Figure 9. Scatter plots of visibility vs (a) RH and (b) wind speed in Chongqing (color figure available online).

Figure 10. Scatter plots of visibility vs (a) RH and (b) wind speed in Chengdu (color figure available online).

Table 3a. Correlation matrix of visibility (V), temperature (T), wind speed (WS), relative humidity (RH), pressure (P), PM₁₀, and API (2000–2010) in Chengdu

	V	T	WS	RH	P	PM10
V	1	0.344	0.396	–0.388	–0.352	–0.433
T	—	1	0.114	−0.107	–0.876	–0.195
WS	—	—	1	−0.057	–0.238	–0.185
RH	—	—	—	1	−0.048	–0.100
P	—	—	—	—	1	0.184
PM10	—	—	—	—	—	1
Note: Values in bold correspond to statistically significant correlation coefficients (p < 0.01).

Table 3b. Correlation matrix of visibility (V), temperature (T), wind speed (WS), relative humidity (RH), pressure (P), PM₁₀, and API (2000–2010) in Chongqing

	V	T	WS	RH	P	PM10
V	1	0.499	0.389	–0.608	–0.413	–0.335
T	—	1	0.186	–0.466	–0.878	–0.201
WS	—	—	1	–0.224	–0.259	–0.092
RH	—	—	—	1	0.361	0.058
P	—	—	—	—	1	0.155
PM10	—	—	—	—	—	1
Note: Values in bold correspond to statistically significant correlation coefficients (p < 0.01).

Table 4a. Factor loadings for visibility, meteorological factors, and PM₁₀ pollutants in Chengdu

Component	PC 1	PC 2	PC 3	PC 4
Total variance (%)	38.9	22.2	17.6	14.8
Cumulative variance (%)	38.9	61.2	78.8	93.6
Eigenvalue	1.947	1.111	0.883	0.741
Visibility	–0.558	–0.548	0.379	0.268
T	−0.109	−0.068	0.052	0.987
WS	−0.095	−0.032	0.984	0.045
RH	−0.081	0.965	0.004	−0.033
PM10	0.962	−0.087	−0.060	−0.075
Notes: T: temperature; RH: relative humidity; WS: wind speed; pressure is omitted due to missing of data from 2004 to 2010. Values in bold correspond to statistically significant correlation coefficients (p < 0.01).

Table 4b. Factor loadings for visibility, meteorological factors, and PM₁₀ pollutants in Chongqing

Component	PC 1	PC 2	PC 3
Total variance (%)	47.2	17.5	16.1
Cumulative variance (%)	47.2	64.7	80.9
Eigenvalue	2.834	1.051	0.966
Visibility	−0.410	0.751	−0.207
T	–0.947	0.171	−0.090
WS*	0.017	0.797	−0.103
RH*	0.458	–0.613	−0.354
P*	0.913	−0.142	0.083
PM10	0.128	−0.122	0.946
Notes: T: temperature; RH: relative humidity; WS: wind speed; P: pressure. Values in bold correspond to statistically significant correlation coefficients (p < 0.01).

Table 5. Empirical models of visibility in Chengdu and Chongqing

	Empirical model	Regression coefficient
Chengdu	Vis = 22.504 + 0.082[T] + 1.132[WS] – 0.103[RH] – 2.477ln[PM10]	0.708
Chongqing	Vis = 1.122 + 0.102[T] + 0.020[P] + 1.340[WS] – 0.124[RH] – 2.168ln[PM10]	0.771
Notes: T: temperature; RH: relative humidity; WS: wind speed; P: pressure.

Figure 11. Scatter plots of simulated and observed daily average visibility in (a) Chengdu and (b) Chongqing during January, April, July, and October of 2011.