[Spatial-temporal Distribution and Evolution Characteristics of Air Pollution in Beijing-Tianjin-Hebei Region Based on Long-term "Ground-Satellite" Data].

This study aimed to promote the coordinated development of regional social economy and ecological environment, build a better living environment, accurately prevent and control pollution, and carry out in-depth surveys and general surveys of air pollution in Beijing, Tianjin, and Hebei. Based on 6 years (June 2014 to December 2019) of ground environmental observation data and satellite data from 2000 to 2019, the distribution characteristics and evolution trend of air pollution in different time and spatial scales were analyzed. The results showed that:① according to the daily average concentration of PM2.5 at the sites, the pollution in the Beijing-Tianjin-Hebei region showed the characteristics of more days, heavy levels, and overall improvement. Pollution mainly occurred from October to April of the following year, accounting for nearly half a year. The pollution level of PM2.5 was the best at Zhangjiakou, followed by Qinhuangdao. ② Based on the 20-year average PM2.5 annual average concentration data retrieved from satellites, the PM2.5 concentration presented a spatial distribution characteristic in which that in the plains was higher than that in mountain area, and PM2.5 concentration in the city was higher than that in the suburbs. PM2.5 concentration changed with time, showing a four-stage bimodal structure of "M"-type evolution characteristics, which gradually increased starting in 2000; the first peak appeared in 2006 and gradually decreased from 2007 to 2012. It rose sharply to the second peak in 2013 and then decreased yearly until 2017. ③ The monthly average AOT data based on satellites every 10 years indicated that the value of AOT in the first time period (2000-2009) was larger than that in the same month of the second time period (2010-2019). The maximum value was in July, and the minimum value was in December. The monthly average AOT in Zhangjiakou and Chengde changed slightly over the past 20 years, and the seasonal and spatial differences were significant in the plain area. ④ Judging from the daily average value of O3-8h observed at the stations, good levels of O3-8h concentrations in the Beijing-Tianjin-Hebei area occurred frequently and widely from March to October. There were at least seven instances of light pollution levels, and the moderate pollution levels and above were not observed. ⑤ The daily average value of SO2 observed on the ground showed that there was no light pollution or above; the good pollution level occurred in winter, and most appeared in the form of pollution for several consecutive days. ⑥ The analysis of AQI data revealed that from 2015 to 2019, the proportion of AQI excellent grades in Beijing increased from 27% to 38%, and the proportion of Tianjin AQI good grades increased from 44% to 64%. The highest proportion of Handan AQI superior grades appeared in 2016, accounting for only 9%. ⑦ The 20-year monthly average concentration of SO2 data based on satellites showed that high-value areas were in Handan, Xingtai, and Shijiazhuang, and low-value areas were in Zhangjiakou and Chengde. The 20-year average NO2 data showed that the high-value centers were in Beijing, Tianjin, Tangshan, Handan, Xingtai, and Shijiazhuang.

[Characteristics of Spatial and Temporal Evolution and Investigation of Air Pollution in Guangdong-Hong Kong-Macao Greater Bay Area Based on Ground-Space Observation Data].

The temporal and spatial distribution characteristics, evolution trend and potential climatic effects of air pollution in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) were analyzed on different time scales and spatial spaces, based on ground environment observation data from June 2014 to December 2018 and satellite remote sensing inversion products from 2000 to 2018. The results show that:① From the in-situ observed daily average concentration of PM2.5, good or mild to moderate pollution occurred in January, February, October, November, and December every year, and the rest of the time was excellent. ② Based on the annual average PM2.5 concentration obtained by satellite for the past 20 years, the spatial characteristics showed that the external radiation is centered on Guangzhou and Foshan. The time evolution shows the characteristics of an Ω shape, which increases gradually from 2000 to 2009, is highest in 2008, and then gradually decreases. ③ The monthly average aerosol optical thickness (AOT) value from the Multi-angle Imaging Spectro Radiometer satellite reversion every 10 years for a period (2000-2009 for a period, 2010-2018 for a period) was used to see the monthly variation. The monthly average AOT value in the first period was larger than that in the second period of the same month, the maximum value was in March and April, and the minimum value was in November and December. It is envisaged to draw a line along the north-south direction of the Pearl River Port, which basically shows that the AOT value in the west is greater than that in the east. ④ According to the observed daily average concentration data of O3-8h, the main concentration level of O3-8h in the GBA is excellent. The cities with good ozone concentration were most numerous in 2014, with five cities, and least in 2018, with only one city. The highest ozone concentration was in September, followed by June and November, and then May and July. In the past 20 years, the spatial distribution of the average concentration of O3 monitored by satellite remote sensing showed a characteristic Ω shape, increasing initially and then decreasing. The maximum value was in May, and the north-south boundary line appeared in space. ⑤ There is a good linear relationship between the interannual variation of monthly mean temperature and radiation, whereas the relationship between AOT and radiation cannot be described by a simple linear relationship.

[Research on the Pollution Characteristics and Causality of Haze-sand Air Pollution in Beijing in Spring].

From May 3 to 5, 2017, a special heavy pollution event occurred in Beijing. The meteorological conditions associated with the heavy pollution were relatively special, so the pollution forms and causes were studied. The general characteristics of this pollution event were obtained based on data from 35 environmental monitoring stations in Beijing. Matching characteristics of PM10 and PM2.5 concentrations with ground wind field data from automatic weather stations closest to the environmental monitoring stations were analyzed. By using MODIS and CALIPSO data, the spatial distribution in the horizontal and vertical directions was obtained, and the transport paths and pollutant categories of the pollution were elucidated. The causes of the pollution were analyzed by using ECMWF ERA-Interim data and Wind Profiler radar data. It was hoped that the special morphological characteristics and influencing factors of the pollution could be obtained by means of ground-space monitoring technology combined with meteorological conditions. The results showed that pollution characteristics and constraints could be better reflected by stereo observations and comprehensive analyses based on the above multi-source data. The pollution started abruptly and dropped sharply, and the pollution process lasted for about 30 hours. The whole process was divided into the following three stages:the first half, intermittent period, and second half. The concentrations of PM10 and PM2.5 were high throughout the whole process, reaching to 600-1000 µg·m-3 and 200-700 µg·m-3, respectively. The causes of pollution in the first half and second half and the resulting PM10 and PM2.5 concentrations were different in terms of the spatial distribution. In the first half, the dominant wind direction was northwest wind, and the wind speed was small. The spatial difference of PM10 concentrations was also small, with concentrations more than 800 µg·m-3; meanwhile, the spatial difference of PM2.5 concentrations was great. The concentration of PM2.5 was high in the south and urban areas, reaching to 600-700 µg·m-3, and it was low in other places, reaching to 350-500 µg·m-3. During the intermission, the wind direction in the lower layer shifted from northwest wind to south wind, and the upper layer maintained northwest wind. The concentration of PM10 in the south and urban area decreased obviously to 650 µg·m-3, and the concentration of PM10 in the north remained at 800 µg·m-3. At this time, the concentration of PM2.5 in the north even dropped to 200 µg·m-3. The dominant wind returned to northwest wind in the latter half, and the wind speed increased sharply. At this time, the spatial difference of PM2.5 concentrations was small and the concentration of PM2.5 at the same station was less than that in the former half, ranging from 250 to 500 µg·m-3. The PM10 concentrations returned to the level of 800 µg·m-3. The pollution process involved mixed pollution consisting of haze and sand. Under the influence of westerly winds, the main contribution to Beijing pollution was dust-type PM10, while under southerly flows, the contribution to Beijing pollution was not only dust, but also PM2.5. Heavy pollution was accompanied by high wind speeds. The vertical motion of the atmosphere converged at an altitude of about 2-3 km, which resulted in the accumulation of pollutants at this altitude.

[Influence of Mountain Valley Breeze and Sea Land Breeze in Winter on Distribution of Air Pollutants in Beijing-Tianjin-Hebei Region].

To reveal the effect of Mountain Valley Breeze (MVB) and Sea Land Breeze (SLB) in winter on the spatial-temporal distribution of air pollutants in the Beijing-Tianjin-Hebei region, hourly data from Automatic Weather Stations (AWS) and hourly air pollutant concentration data in December 2016 from the China National Environmental Monitoring Center were used to calculate the average wind vector fields and PM2.5 concentration fields. The change rule of MVB and SLB and its influence on the distribution of PM2.5 concentration were analyzed. The prevailing factor for the MVB days was the southerly wind (valley wind) in the Beijing-Tianjin-Hebei region from noon to afternoon, this valley wind transports air pollutants from the eastern areas of the Taihang Mountains and southwestern areas of Beijing northward. In the evening, "herringbone" convergence lines formed between the emerging mountain breeze in the western and northern parts of Beijing, as well as in the piedmont areas of the Taihang Mountains, and the southerly wind. The PM2.5 concentration increased in Beijing, Langfang, Baoding, Shijiazhuang, and Xingtai according to the concentration of the convergence lines. For the SLB days, the PM2.5 concentration increased in the piedmont areas of the Taihang Mountains due to the influence of valley wind from noon to afternoon. For the MVB days, from noon until midnight, the sea breeze appeared in the eastern coastal areas and reached the southeastern part of Tianjin, the PM2.5 concentration increased towards the front of the sea breeze. The influence of MVB and SLB on the distribution of air pollutants in the heavy pollutions process were surveyed by analyzing the temporal variation relationship between the vertical distribution of wind over 0-325 m tower (at the Institute of Atmospheric Physics) and PM2.5 concentration of urban area, and by using the Cressman method to interpolate the 10 m wind data and PM2.5 concentration data to 2D grid field. From noon to afternoon, the air pollutants were blown to Beijing by valley wind. In the evening, the air pollutants converged near the convergence lines, which were formed by the mountain breeze and southerly wind. The severe pollution zone formed in the plains of Beijing and south of Beijing. From midnight to early morning, the air pollutants in Beijing were gradually blown away by the mountain breeze and stayed south of Beijing and northwest of Tianjin. In the winter, the effect of MVB on the recycling and accumulation of air pollutants plays an important role in severe atmospheric pollution incidents in Beijing, south of Beijing, and the eastern areas of the Taihang Mountains.