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We compare the probability density function (PDF) of CO2 and NOx emissions between the urban and suburban CNEMC sites (Fig. 2). For CO2, 76% of suburban sites show emissions lower than 10000 t km−2 yr−1, and this percentage is higher than that of urban sites (65%). In contrast, 19% of urban sites have emissions higher than 20000 t km−2 yr−1, which is only 5% for the suburban sites. For NOx, 80% of suburban sites show low NOx emissions of less than 20 t km−2 yr−1, and this percentage is also higher than that of urban sites (65%). The PDF shows that anthropogenic emissions are generally higher in urban than suburban areas, suggesting different pollution levels between urban and suburban regions.
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We focus on air pollution in the summer (June–July–August, JJA) and winter (December–January–February, DJF) during 2015–18. Figures 3a–b show the urban and suburban [MDA8] in the four regions. On average, the [MDA8] is higher in summer, with the highest level in BTH and the lowest in PRD (Fig. 3a). In contrast to summer, both the urban and suburban [MDA8] shows a peak in PRD but low values in BTH in winter. The low summertime MDA8 in South China is associated with large quantities of precipitation that wash out precursors in this season (Wang et al., 2017), while the high summertime MDA8 in North China is related to the high temperatures and solar radiation (Zhao et al., 2019).
Figure 3. Comparison of (a, b) MDA8 O3 (units: ppbv) and (c, d) PM2.5 concentrations (units: μg m−3) in (a, c) summer (JJA) and (b, d) winter (DJF) between urban (blue) and suburban (red) sites from 2015 to 2018 in four regions. Each box plot represents the median (middle line), 25th and 75th percentiles (upper and lower boundaries), and the range of summer or winter levels among different sites in a region. The stars show outliers for each region.
Figures 3c–d show the urban and suburban [PM2.5] in summer and winter, respectively. In summer, [PM2.5] is highest in BTH and lowest in PRD, consistent with the distribution of [O3] in the same season. In winter, the lowest urban and suburban [PM2.5] are found in PRD, but the highest values are found in BTH for urban and YRD for suburban areas. Such a winter distribution of [PM2.5] generally resembles its summer pattern, except that both the average level and variability are much larger in the cold seasons. The lowest urban and suburban [PM2.5] in PRD are related to fewer coal-based industries and favorable meteorological conditions for atmospheric dispersion and dilution (Zhang and Cao, 2015). In comparison, the highest [PM2.5] in BTH is associated with the stagnant weather (Chen et al., 2008), high local emissions (Zhang and Cao, 2015), and frequent regional transportation (Huang et al., 2014).
To quantify the urban-to-suburban differences of air pollution, we subtract the average concentration of all suburban sites from that of urban sites in the same region and detect the significance of the difference using the Student’s t-test (significance level:
$ P $ < 0.05) (Fig. 4 and Table S1). The Δ[MDA8] is negative for almost all regions, indicating that the suburban [MDA8] is higher than that in urban areas, except for YRD in summer (Δ[MDA8] = 2.7 ppbv). For YRD, high emissions of biogenic and anthropogenic VOCs (Liu et al., 2018) and the substantial NOx reductions (He et al., 2017; Song et al., 2017) convert a VOC-limited regime to a mixed sensitive environment (Jin and Holloway, 2015), leading to a positive (though nonsignificant) urban-to-suburban Δ[MDA8] via the higher urban NO2 level (Fig. 4c). In contrast to MDA8, the Δ[PM2.5] is generally positive in the four regions (Fig. 4b), suggesting that concentrations of urban PM2.5 are usually higher than in suburban areas. Negative but nonsignificant Δ[PM2.5] values of −0.04 (summer) and −0.6 μg m−3 (winter) are found in YRD.Figure 4. The urban-to-suburban differences in concentrations of (a) MDA8 O3 (units: ppbv), (b) PM2.5 (units: μg m−3), (c) NO2 (units: ppbv), (d) SO2 (units: ppbv), (e) NO2 to O3 ratio, and (f) PM2.5 to PM10 ratio, in summer (left-hand bars) and winter (right-hand bars), from 2015 to 2018, in four regions. The black dots denote that the difference is statistically significant P < 0.05).
We calculate the urban-to-suburban differences in the NO2, SO2 and NO2 to O3 ratio, and the PM2.5 to PM10 ratio (Figs. 4c–f), to determine the possible reasons for the differences of MDA8 and PM2.5. It should be noted that the [MDA8] between urban and suburban areas is significantly different only in BTH (−7.0 ppbv) and SCB (−6.3 ppbv) during winter. In these two regions, the urban NO2 concentrations are significantly higher than the suburban ones by 6.0–10.0 ppbv (Fig. 4c). As O3 can be titrated by NO via the reaction NO + O3
$ \to $ NO2 + O2 (Sillman, 1999; Murphy et al., 2007), the higher level of NO2 in urban areas indicates strong conversions from NO to NO2 (Tong et al., 2017), leading to higher O3 loss and lower [MDA8] in urban areas (Fig. 4a). This is also evidenced by the highest NO2 to O3 ratios over urban sites in BTH and SCB (Fig. 4e). In BTH, the urban [PM2.5] during winter is significantly higher than that observed in suburbs (32.7 μg m−3), which is mainly due to secondary production. Different from the three other regions, Δ[SO2] and Δ[NO2] in BTH are much higher (Figs. 4c–d). Furthermore, the PM2.5 to PM10 ratio over urban sites is larger than in the suburbs (Fig. 4f), suggesting that secondary formation of fine particles contributes more than primary emissions in the urban areas of BTH. -
We quantify the diurnal, weekly, seasonal, and interannual variations of the urban-to-suburban differences of O3 and PM2.5 in the four regions (Fig. 5). The hourly [O3] is used to study the diurnal variation (Fig. 5a). During daytime, the absolute Δ[O3] peaks at 0800 LST (local standard time) in most sub-regions, especially for BTH (−8.7 ppbv) and SCB (−5.5 ppbv), likely due to high NOx emissions from traffic in the rush hour (Dominguez-Lopez et al., 2014). Traffic emissions are also an important driver for Δ[PM2.5], the peak of which (21.0 μg m−3) is found at 0800 LST in BTH (Fig. 5e, Table S2). In addition, high values of Δ[PM2.5] may be caused by relatively low boundary-layer heights (Zhang and Cao, 2015) and weaker turbulence (Miao et al., 2016) in urban areas.
Figure 5. (a, e) Diurnal, (b, f) weekly, (c, g) seasonal and (d, h) interannual variations of urban-to-suburban differences of (a–d) O3 (units: ppbv) and (e–h) PM2.5 (units: μg m−3) from 2015 to 2018 in four regions. The black dots denote that the difference is statistically significant (P < 0.05). The values are shown in Table S2.
We use the daily [O3] to study the weekly variations of urban-to-suburban differences (Fig. 5b). The ozone weekend effect (OWE) indicates that the daily mean [O3] (not [MDA8]) is lower on weekdays than weekends owing to lower anthropogenic NOx emissions at weekends (Tong et al., 2017). However, our results do not find the OWE in all sub-regions, except for the urban areas in YRD and PRD, where the differences between weekday and weekend [O3] are nonsignificant (Table S3). For suburban areas, a positive Δ[O3] between weekdays and weekends is found, with a maximum difference of 6.8 ppbv in YRD. No significant differences of Δ[PM2.5] are found between weekdays and weekends (Fig. 5f).
Both the Δ[MDA8] (Fig. 5c) and Δ[PM2.5] (Fig. 5g) show seasonal variation in the four regions. The year-round Δ[MDA8] is generally negative, except in spring and summer in YRD, which may be related to the nonlinear relationship of precursor emissions (Liu et al., 2018). In contrast, most Δ[PM2.5] values are positive, except for YRD. The absolute values of Δ[MDA8] and Δ[PM2.5] usually show peaks in winter and lows in summer, when there are more rainy days in BTH and SCB.
We further examine the interannual variations of Δ[MDA8] (Fig. 5d) and Δ[PM2.5] (Fig. 5h). The absolute Δ[MDA8] exhibits a decreasing trend over BTH and PRD but an increasing trend in SCB during 2015–18. For YRD, the value of Δ[MDA8] shifts from positive to negative after the year 2016. The values of Δ[PM2.5] generally decrease in all regions. In YRD, the Δ[PM2.5] is positive during 2015–16, but has become negative since 2017, though its magnitude is close to zero (Table S2). On an annual mean basis, the suburban [MDA8] is higher than the urban value by 3.7 ppbv in BTH, 3.5 ppbv in PRD, and 3.8 ppbv in SCB. In comparison, the [PM2.5] in suburban areas is lower than the urban value by 15.8 μg m−3 in BTH, 3.5 μg m−3 in PRD, and 2.4 μg m−3 in SCB.
The variations of urban-to-nonurban differences of air pollution are related to the ambient pollution levels. Figure 6 illustrates the variations of Δ[MDA8] at different ranges of urban [MDA8] on a daily basis from 2015 to 2018. In BTH and SCB, the median Δ[MDA8] shifts from a negative to positive value with an elevated urban [MDA8], suggesting that the increase of [MDA8] at urban sites is faster than at suburban sites. In summer, the urban [MDA8] can be either high (e.g., sunny days) or low (e.g., rainy days) on different days. As a result, the positive and negative Δ[MDA8] values may offset each other, leading to a limited average Δ[MDA8] (Fig. 4a). In winter, the urban [MDA8] is usually low, leading to a strong and negative Δ[MDA8] in these sub-regions (Fig. 5c). In comparison, the Δ[PM2.5] changes from near zero to more positive values with the increase of urban [PM2.5] in all four regions (Fig. 7). As the [PM2.5] rises, there is an overall increasing trend and variability of Δ[PM2.5]. This suggests that the [PM2.5] in urban areas grows faster compared to in suburban areas during pollution episodes, and the Δ[PM2.5] is linearly dependent on the urban [PM2.5]. As a result, the Δ[PM2.5] shows large positive values during winter season, when the urban [PM2.5] is usually high (Fig. 4b).
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In total, there are 10 background sites in the central-eastern China region (18°–43°N, 100°–125°E), the number of which is much smaller than that of urban and suburban sites. Here, we compare the annual mean [MDA8] and [PM2.5] at these background sites to the nearby urban and suburban sites within a 2° × 2° grid cell (Table 1 and Table S4). On average, the background [MDA8] (37.8–55.0 ppbv) is higher by 12% than in urban areas, and by 5% than in suburban areas. We find better correlations of [MDA8] between the background and suburban sites (R = 0.8) than those between the background and urban sites (R = 0.4) (Fig. 8). In contrast, the [PM2.5] over background sites (6.7–33 μg m−3) is lower by 45% than in urban areas, and by 30% than in suburban areas (Fig. 9), with a higher correlation coefficient between background and suburban sites (R = 0.8). As for the regression fits, suburban values are closer to the background concentrations for O3, consistent with the findings in previous studies (Tong et al., 2017; Huang et al., 2018).
Site ID Name MDA8 Urban MDA8 Suburban MDA8 PM2.5 Urban PM2.5 Suburban PM2.5 1 Pangquangou, Shanxi 51.2 37.8–43.0 – 19.7 53.0–74.7 – 2 Wuyishan, Fujian 50.8 37.9–44.5 41.4–42.6 17.3 22.7–46.0 32.7–41.0 3 Changdao, Shandong 38.8 42.8–54.8 – 33.0 29.5–47.3 – 4 Shenlongjia, Hubei 47.0 38.7 42.6 9.2 56.5 31.5 5 Hengshan, Hunan 48.7 34.0–43.9 39.5–45.3 23.5 39.1–60.0 43.3–47.7 6 Nanling, Guangdong 46.9 43.0–46.8 – 13.3 34.8–42.9 – 7 Wuzhishan, Hainan 37.8 31.5–33.8 – 11.2 14.6–15.6 – 8 Hailuogou, Sichuan 39.3 – – 6.7 – – 9 Lijiang, Yunnan 39.0 33.5–42.0 40.2 7.6 11.5–15.2 17.1 10 Menyuan, Qinghai 55.0 46.5–51.3 52.4 12.7 25.7–39.7 42.5 Table 1. Information on 10 background sites in central-eastern China (18°–43°N, 100°–125°E), including name, annual mean [MDA8] (ppbv), and [PM2.5] (μg m−3), and the range of concentrations for nearby urban and suburban sites within a 2° × 2° grid cell. The numbers and distances of the nearby sites are shown in Table S4.
Figure 8. Comparison of annual mean MDA8 O3 concentrations (units: ppbv) at 10 background sites to nearby urban and suburban sites within a 2° × 2° grid cell in central-eastern China (18°–43°N, 100°–125°E) in 2017. The numbers from 1 to 10 correspond to those in Table 1.
Figure 9. Comparison of annual mean PM2.5 concentrations (units: μg m−3) at 10 background sites to nearby urban and suburban sites within a 2° × 2° grid cell in central-eastern China (18°–43°N, 100°–125°E) in 2017. Data of site 1 is outside the axis range.
We further calculate the Δ[MDA8] and Δ[PM2.5] for summer (JJA) and winter (November–December, ND) between urban and background sites in 2017. Results show that the absolute urban-to-background (urban minus background) differences of [MDA8] and [PM2.5] are much larger in winter than summer (Fig. 10), consistent with the seasonal variations of urban-to-suburban differences (Fig. 4). In summer, a moderate contrast of air pollution (−5.1 to 6.8 ppbv for Δ[MDA8] and −0.1 to 22.5 μg m−3 for Δ[PM2.5]) is found between urban and background sites (Table S5). However, such a contrast is much larger and more significant in winter (−22.2 to 5.5 ppbv for Δ[MDA8] and 3.1 to 82.3 μg m−3 for Δ[PM2.5]). Exceptions of positive Δ[MDA8] are found at sites 4, 5, 6 and 9 in JJA (Fig. 10a), suggesting that the sign of urban-to-background Δ[O3] is not uniform on the country level during summer.
Figure 10. The urban-to-background differences in concentrations of (a, b) MDA8 O3 (units: ppbv) and (c, d) PM2.5 (units: μg m−3) in (a, c) summer (JJA) and (b, d) winter (ND) of 2017 between 10 background sites and their surrounding urban sites within a 2° × 2° grid cell. The numbers from 1 to 10 correspond to those in Table 1. The black dots denote that the difference is statistically significant (P < 0.05).