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Ambient PM 2.5 pollution has been a major environmental concern in recent years. Beijing, the capital of China, is enduring frequent and severe PM 2.5 pollution. In this study, 186 valid PM 2.5 pollution episodes during the 2014-2017 period were identified and classified into four categories according to the mechanism of pollution formation and evolution. Category I often occurs in autumn, winter and early spring, depending on accumulation during stagnant weather. Category II featured by photochemistry is dominant in summer, and category III caused by dust storms occasionally occurs in spring. Category IV represents a combination during transition periods. Interannual variations show that particulate pollution decreased from 2014 to 2017, and the decline in categories I and II played the most important role. To further understand the PM 2.5 pollution patterns in Beijing, the temporal and spatial characteristics and relationships between PM 2.5 levels and meteorological features were analyzed. Category I is the main pollution type that brings forth heavy or severe pollution and has the longest duration in those cases, while category II often leads to light or moderate pollution. There is a north-south gap in the PM 2.5 levels in Beijing. The high-level pollution in category I tends to evolve northward, while the low-level pollution in category I and category II pollution tend to evolve southward and widen the north-south gap, affected by the regional transport and more local emissions in the south. Additionally, the relationship between the concentrations and meteorology also vary with the pollution categories. High relative humidity, low wind speeds and low boundary layer heights are likely to lead to category I or II pollution, but category III requires high winds. These results provide insights into the annual tendency and characteristics of Beijing's particulate pollution in recent years. Image 1 • The PM 2.5 pollution episodes from 2014 to 2017 were identified and classified. • Particle pollution has been decreasing in recent years due to the decline in accumulation and photochemistry types. • Most of the heavy and severe pollution is due to the type of accumulation. • There is a north-south gap in PM 2.5 levels in Beijing. • The relationships between PM 2.5 levels and meteorological features vary with pollution categories. [ABSTRACT FROM AUTHOR]

Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space arrangement on the diffusion of air pollution, but these studies have neglected the patterns at the landscape scale. Other studies have analyzed the effects of the large scale of green space on air pollution, but the vertical distribution of street buildings and greening has rarely been considered. In this study, we analyzed the impact of the vertical distribution of urban street green space on summertime air pollution in urban centers on the urban scale for the first time by using a deep-learning method to extract the vertical distribution of street greening and buildings from street view image data. A total of 687,354 street view images were collected. The green index and building index were proposed to quantify the street greening and street buildings. The multilevel regression method was used to analyze the association between the street green index, building index and air pollution indexes. For the cases in this study, including the central urban areas of Beijing, Shanghai and Nanjing, our multilevel regressions results suggested that, in the central area of the city, the vertical distribution of street greening and buildings within a certain range of the monitoring site is association with the summertime air pollution index of the monitoring site. There was a significant negative association between the street greening and air pollution indexes (radius = 1–2 km, NO2, p = 0.042; radius = 3–4 km, AQI, p = 0.034; PM10, p = 0.028). The street length within a certain range of the monitoring site has a positive association with the air pollution indexes (radius = 1–2 km, AQI, p = 0.072; PM10, p = 0.062). With the increase of the distance between streets and the monitoring sites, the association between streets and air pollution indexes decreases. Our findings on the association between the vertical structure of street greening, street buildings and summertime air pollution in urban centers can support urban street planning. [ABSTRACT FROM AUTHOR]