Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign

Abstract

Abstract. We examine the street-scale variation of NOx, NO2, O3 and PM2.5 concentrations in Beijing during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) winter measurement campaign in November–December 2016. Simulations are performed using the urban air pollution dispersion and chemistry model ADMS-Urban and an explicit network of road source emissions. Two versions of the gridded Multi-resolution Emission Inventory for China (MEIC v1.3) are used: the standard MEIC v1.3 emissions and an optimised version, both at 3 km resolution. We construct a new traffic emissions inventory by apportioning the transport sector onto a detailed spatial road map. Agreement between mean simulated and measured pollutant concentrations from Beijing's air quality monitoring network and the Institute of Atmospheric Physics (IAP) field site is improved when using the optimised emissions inventory. The inclusion of fast NOx–O3 chemistry and explicit traffic emissions enables the sharp concentration gradients adjacent to major roads to be resolved with the model. However, NO2 concentrations are overestimated close to roads, likely due to the assumption of uniform traffic activity across the study domain. Differences between measured and simulated diurnal NO2 cycles suggest that an additional evening NOx emission source, likely related to heavy-duty diesel trucks, is not fully accounted for in the emissions inventory. Overestimates in simulated early evening NO2 are reduced by delaying the formation of stable boundary layer conditions in the model to replicate Beijing's urban heat island. The simulated campaign period mean PM2.5 concentration range across the monitoring network (∼15 µg m−3) is much lower than the measured range (∼40 µg m−3). This is likely a consequence of insufficient PM2.5 emissions and spatial variability, neglect of explicit point sources, and assumption of a homogeneous background PM2.5 level. Sensitivity studies highlight that the use of explicit road source emissions, modified diurnal emission profiles, and inclusion of urban heat island effects permit closer agreement between simulated and measured NO2 concentrations. This work lays the foundations for future studies of human exposure to ambient air pollution across complex urban areas, with the APHH-China campaign measurements providing a valuable means of evaluating the impact of key processes on street-scale air quality.

Direct measurements of black carbon fluxes in central Beijing using the eddy-covariance method

“…However, the average flux values between the two seasons were similar, indicating a similar source between seasons. For both seasons, BC diurnals showed similar patterns to that of CO and NOx fluxes (Squires et al, 2020), suggesting that traffic emissions are the dominant source of BC in this region of Beijing. Therefore, we additionally averaged BC fluxes according to wind sector, and generated polar plots using OpenAir (Carslaw and Ropkins, 2012) for mass fluxes, as shown in Figure 9.…”

“…Compared to the observed ratios, these are factors of 9.4 and 6.5 too high for the ratios with CO in winter and summer respectively, and 7.3 and 8.1 for the ratios with NOx. This implies that the BC emissions from traffic are overestimated in MEIC, although it should be noted that the emissions of NOx and CO from the inventory are also high compared with measured fluxes, as reported by Squires et al (2020). The overestimation of emissions in this region may reflect the suitability of the proxies used in downscaling the emissions from a regional, provincial resolution to the 3-km scale used here, as noted by Zheng et al (2017).…”

“…CO. During winter, cold-start condition may explain the increase in CO emission which is further discussed by (Squires et al, 2020). The comparison with NOx fluxes shows BC/NOx ratios are the same between seasons, 0.0052 and 0.0056 for winter and summer, respectively.…”

“…Figure 7 depicts the extent of the average flux footprint for the winter and summer measurement periods, respectively. These footprints were generated using the method presented by Squires et al (2020), using the footprint model and theory discussed by Kljun et al (2004) and Metzger et al (2012). However, the total numbers of individual footprints used to generate average campaign footprints are slightly different to those presented by Squires et al (2020) owing to slight differences in the respective instrument uptimes.…”

“…In this study, BC emissions for year 2013 at a resolution of 3 × 3 km 2 were used, which was the most recent version available at the time of this study. The inventory emission values were extracted for the area covered by the averaged flux footprint (see Section 4.3) following the same approach as Squires et al (2020). Figure 11 shows a comparison of the diurnal cycles between measured fluxes and the MEIC 2013 emission inventory for (a)…”