Influence of circulation types on temporal and spatial variations of ozone in Beijing.

This study analyzes the impact of circulation types (CTs) on ozone (O3) pollution in Beijing. The easterly high-pressure (SWW) circulation occurred most frequently (30%; 276 day), followed by northwesterly high-pressure (AN) circulation (24.3%; 224 day). The SWW type had the highest O3 anomaly of +17.28 µg/m3, which was caused by excellent photochemical reactions, poor diffusion ability and regional transport. Due to the higher humidity and precipitation in the low-pressure type (C), the O3 increase (+8.02 µg/m3) was less than that in the SWW type. Good diffusion/wet deposition and weak formation ability contributed to O3 decrease in AN (-12.54 µg/m3) and northerly high-pressure (ESN) CTs (-12.26 µg/m3). The intra-area transport of O3 was significant in polluted circulations (SWW- and C-CTs). In addition, higher temperature, radiation and less rainfall also contributed to higher O3 in northern Beijing under the SWW type. For the clean CTs (AN and ESN CTs), precursor amount and intra-area transport played a dominant role in O3 distribution. Under the northeasterly low-pressure CT, better formation conditions and higher precursor amount combined with the intra-area southerly transport to cause higher O3 values in the south than in the north. The higher O3 in the northwestern area under the northeasterly high-pressure type was influenced by weaker titration loss and high O3 concentration in previous day. Annual variation in the CTs contributed up to 86.1% of the annual variation in O3. About 78%-83% of the diurnal variation in O3 resulted from local meteorological factors.

On-road mobile mapping of spatial variations and source contributions of ammonia in Beijing, China.

Ammonia (NH3) measurements were performed with a mobile platform deploying a cavity ring-down spectroscopy NH3 analyzer in Beijing. The transect and loop sampling strategy revealed that the Beijing urban area is more strongly affected by NH3 emissions than surrounding areas. Although average enhancements of on-road NH3 were small compared to background levels, traffic emissions clearly dominated city enhancements of NH3, carbon dioxide (CO2), acetaldehyde and acetone. Increments of on-road NH3 ranged between 5.1 ppb and 11.4 ppb in urban areas, representing an enhancement of 20.6 % to 47.9 % over the urban background. The vehicle NH3:CO2 emission ratio was 0.26 ppb/ppm, about a factor of 1.5 higher than the value derived from the available emission inventory. The obtained NH3 emission factor was approximately 306.9 mg/kg. If the annual gasoline consumption in Beijing is accurate, annual NH3 emissions from vehicles are estimated at 1.5 Gg. The influx and outflux of NH3 in Beijing during monitoring periods fluctuated due to variations of wind direction (WD), wind speed (WS), and planetary boundary layer height (PBLH). Net fluxes at the 4th Ring Road were larger than zero, suggesting that local emissions were important in urban Beijing. Negative net fluxes at the 6th Ring Road reveal a large amount of NH3 transported from agricultural regions south of Beijing lost during transport across the city, for example by deposition or particle formation in the city. Our analyses have important implications for regional NH3 emission estimates and for improving vehicular NH3 emission inventory allocations.

[Characteristics of Two Pollution Episodes Before and After City Heating in Beijing from February to March of 2019].

The characteristics of meteorological conditions and pollutant concentrations were analyzed based on two pollution episodes before and after city heating in Beijing during February to March of 2019. The backward trajectory and WRF-CAMx models were used to analyze the evolution of pollutants before and after city heating, and the influences of meteorological conditions, regional transport, and secondary transformation on the episodes were discussed. There was little difference in the average ρ(PM2.5) between February 21-24 (episode 1) and March 18-20 (episode 2), with concentrations of 100.1 µg·m-3 and 97.2 µg·m-3, respectively. However, compared with that of episode 2, in episode 1 the average peak value was higher with two peak stages, the diurnal variation was clearer, and the process developed much more rapidly. Moreover, episode 1 was regional pollution, while episode 2 was more related to local pollution in Beijing. The SO2 concentrations in both episodes were not higher than 16 µg·m-3, thereby indicating the effectiveness of coal-burning treatment and other measures. In addition, two peaks occurred in the diurnal fluctuation of SO2 in episode 1, whereas only one peak occurred for episode 2. In episode 1, the CO concentration was high and the ratio of ρ(CO)/ρ(SO2) increased around February 22-23 (phase 1); moreover, the pollutant concentrations in the central and southern areas of the Beijing-Tianjin-Hebei region and those in the background sites located in the southern part of the Beijing plain were higher than those in the urban area, thereby indicating that the diffusion conditions of episode 1 were unfavorable and the first PM2.5 peak was mainly affected by regional transport. A high ratio of ρ(PM2.5)/ρ(CO) in episode 2 suggested a slightly larger proportion of secondary generation for PM2.5, whereas higher ratios of ρ(NO2)/ρ(CO), ρ(SO2)/ρ(CO), and ρ(SO42-)/ρ(PM2.5) in episode 2 and the similar SOR value to that of episode 1 demonstrated that episode 1 was more advantageous for gas phase transformation and episode 2 was more affected by the coal industry. Phased analysis of episode 1 showed that the indicators of second generation for PM2.5 in phase 2 (around February 23-24) of episode 1 and episode 2 were similar, and both were higher than that in phase 1 of episode 1, which implied that the second PM2.5 peaks of episode 1 and episode 2 were mainly related to local emissions and chemical conversion. Both WRF-CAMx with and without assimilation experiments could better reproduce the temporal variation in pollutants, and the correlation between the simulation and observations increased but with lower values after assimilation. The model performance for the PM2.5 trend simulation significantly increased with data assimilation, and the simulated lower NO2 in February and higher NO2 in March as well as the overestimated SO2 were also improved. In addition, the pollutant concentration simulation in Beijing was more sensitive to that of Hebei in episode 1, which suggested that episode 1 was more affected by regional transport. The simulation ability for the rapid growth of pollutants needs to be promoted, and the response of pollutant types to emission reduction and the feedback related to the atmospheric oxidant and aerosol properties may be important for the simulation effect, which all require further study.

[Differences in Pollution Characteristics Under the Southerly and Easterly Wind in Beijing].

In this study, the hourly meteorological factors and PM2.5 concentrations during 2014-2019 in Beijing were analyzed, in order to explore the characteristics of the prevailing wind direction of pollution, and the corresponding long-term tendency. During the study period, 67% of pollution in Beijing occurred under the influence of southerly and easterly wind, and pollution was most likely to occur in winter, followed by spring and autumn. The average pollution probability of winter, spring, autumn and summer was 45.2%, 34.1%, 32.1%, and 26.1% and 47.0%, 45.8%, 39.7%, and 29.6% for southerly and easterly wind, respectively. In Beijing, the southerly wind appeared more frequently, but the pollution occurrence probability was higher under the control of easterly wind, with the maximum difference of 11.7% (2.8%-18.6%) in spring and the minimum difference of 1.8% (-7.6%-13.9%) in winter. During the past six years, the pollution probability decreased at a rate of 4.6%-8.0% and 5.5%-7.9% per year under the southerly and easterly wind influence, respectively. This was clearly reflected in reduced moderate and above levels of pollution. An analysis of both the pollution and meteorological factors under the two wind directions indicates that the visibility, mixing layer height, wind speed, and the frequency of hourly wind speed greater than 3 m·s-1 were higher, and the relative humidity and dew point temperature were lower, when pollution occurred under the southerly wind, while the PM2.5 concentration of pollution was higher in winter and significantly lower in other seasons compared to that of the easterly wind. These findings show that when pollution occurred under the southerly wind, the carrying capacity and diffusion capacity of pollutants in the atmosphere was slightly better than that of the easterly wind, and the increased atmospheric water content under the easterly wind was more conducive to the maintenance and aggravation of pollution. Moreover, under the background of original emission levels, when adding urban heating in winter, the air mass transported by the southerly wind may be more conducive to increased PM2.5 concentration. Furthermore, pollution in Beijing tended to be an "easterly wind type" in spring, summer and autumn, but remained a "southerly wind type" in winter.

Contributions of inter-city and regional transport to PM2.5 concentrations in the Beijing-Tianjin-Hebei region and its implications on regional joint air pollution control.

Regional transport plays an important role in the serious PM2.5 pollutions in the Beijing-Tianjin-Hebei (BTH) region, China. Practical regional joint emission control strategies require quantitative assessments of the transport contribution among cities and regions. The Community Multiscale Air Quality model equipped with the Integrated Source Apportionment Model is used to simulate the contributions from 5 major emission sectors in 13 cities of the BTH region, and 4 surrounding provinces outside BTH for the year 2014. Annual averaged local contribution ranges from 32% to 63% for the 13 cities in the BTH region, where secondary components contributing more than primary components. Regional contribution ratio becomes larger and the transport distance longer in July and October than in January and March. For Beijing, local contributions are 62% and 69% in January and March respectively, and the regional transports are mainly from nearby cities such as Zhangjiakou, Baoding and Langfang. In July and October, local contributions in Beijing are only 33% and 38% respectively, and a large range of regions in the south have substantial contributions, where Shandong Province and Henan Province contribute 3.6-5.3 µg/m3. Analysis of daily contributions suggests that regional transport is stronger under higher PM2.5 concentrations. During heavy pollution, local emissions in Beijing contribute 61%, 49%, 23% and 25% in January, march, July and October respectively, while during the clean days, the ratios are 88%, 88%, 76% and 57% respectively. Southerly regional transport during the rising phase of "saw tooth" pattern might be enhanced by weak cold high pressure and its easterly, northerly moving path. Among the major emission sectors, in winter, local domestic combustion is the most important source for Beijing, Tianjin and Shijiazhuang. In summer, transportation and domestic combustion are two important local sources for Beijing, while joint control in other cities should focus on industry.

Estimating the contribution of regional transport to PM2.5 air pollution in a rural area on the North China Plain.

PM2.5 air pollution in metropolises as well as some medium-sized cities in the North China Plain have aroused many researchers' interest, but less attention has been paid to the rural areas of this region. In this study, four months of daily PM2.5 samples were collected from a rural site in Lingcheng (a district of Dezhou City in Shandong Province) during different seasons in 2013 and 2014. Analysis of the samples indicates that the PM2.5 air pollution was severe over this area with the four-month average concentration of 105.9µg/m3, three times higher than China's guideline for this pollutant (35µg/m3). In winter, the monthly average concentration was as high as 151.2µg/m3. In order to identify the potential source regions, the Integrated Source Apportionment Method within Community Multiscale Air Quality model (CMAQ-ISAM) was applied during the wintertime. The regional source apportionment results show that local emissions in Lingcheng only contributed 15.4% to PM2.5 concentrations, with 12.6% and 28.1% from its circumjacent areas in Dezhou City and the six surrounding cities, respectively. Regional transport from areas farther away and the boundaries account for 31.6% and 11.1%, respectively. This indicates that the ambient PM2.5 at Lingcheng is not affected only by emissions from local and circumjacent areas; regional and long-range transport should also be considered. Further analysis indicated that with increasing degrees of pollution, the contributions from local and circumjacent regions showed a clear downward trend, while the contributions from northern and southwestern areas, which most of the trajectories passed through during periods of heavy haze, showed an obvious upward trend.