Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China

Response of fine particulate matter and ozone concentrations to meteorology and anthropogenic precursors over the "2+26" cities of northern China

Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

The effect of natural and socioeconomic factors on haze pollution from global and local perspectives in China

Analyzing the factors that cause haze and the regional differences in the influence of factors on haze is the premise and critical to precise prevention and control of haze pollution. This paper explores the global effects of haze pollution drivers and the spatial heterogeneity of factors on haze pollution using global and local regression models. The results show that, from a global perspective, a 1 μg/m³ increase in the average PM_2.5 concentration of a city's neighbors will increase the city's PM_2.5 concentration by 0.965 μg/m³. Temperature, atmospheric pressure, population density, and green coverage of built-up areas are positively associated with haze, while GDP per capita is the opposite. From a local perspective, each factor has different influencing scales on haze pollution. Specifically, technical support is on a global scale, and for every 1 unit increase in technical support level, the PM_2.5 concentration will decrease by 0.106-0.102 μg/m³. The influencing scales of other drivers are local. In southern China, the concentration of PM_2.5 decreases by 0.001-0.075 μg/m³ for every 1 °C increase in temperature, while in northern China, the concentration of PM_2.5 increases by 0.001-0.889 μg/m³. In the region around the Bohai Sea in eastern China, the concentration of PM_2.5 will decrease by 0.001-0.889 μg/m³ for every 1 m/s increase in wind speed. Population density positively impacts haze pollution, and the impact intensity gradually increases from 0.097 to 1.140 from south to north. For every 1% increase in the proportion of the secondary industry in southwest China, the PM_2.5 concentration will increase by 0.001-0.284 μg/m³. For cities in northeast China, for every 1% increase in the urbanization rate, the PM_2.5 concentration will decrease by 0.001-0.203 μg/m³. These findings help policymakers develop targeted joint prevention and control policies for haze pollution, considering regional differences.

High spatiotemporal resolution vehicular emission inventory in Beijing-Tianjin-Hebei and its surrounding areas (BTHSA) during 2000-2020, China

One comprehensive emission inventory of CO, HC, NO_X, PM₁₀, PM_2.5, BC, CH₄, CO₂ and N₂O with high spatial resolution (0.01° × 0.01°) for 58 cities in Beijing-Tianjin-Hebei and its surrounding areas (BTHSA) during 2000-2020 are developed by using COPERT model and ArcGIS methodology. The results show that vehicular emissions of CO, HC, NO_X, PM₁₀, PM_2.5, BC and CH₄ have begun to decrease or slow their growth rates in recent years due to the implementation of measures to control vehicular emissions. However, vehicular emissions of CO₂ increase rapidly due to little fuel economy improvement. Besides, the usage of selective catalytic reduction (SCR) systems by heavy duty truck (HDT) is the main factor impacting the growth trend of vehicular N₂O emissions since 2017. By 2020, vehicular emissions of CO, HC, NO_X, PM₁₀, PM_2.5, BC, CO₂, CH₄ and N₂O are estimated at about 1.65 Mt, 0.35 Mt, 1.39 Mt, 87.44 kt, 55.06 kt, 15.57 kt, 527.71 Mt, 36.20 kt and 8.56 kt, respectively. Therein, China III, IV, IV and IV passenger cars (PCs) are the predominated models for vehicular emissions of CO, HC, CH₄ and CO₂, accounting for 19.59-28.26 % of the total vehicular emission of corresponding pollutant. Nevertheless, the major contributors of vehicular emissions of NO_X, PM₁₀, PM_2.5, BC and N₂O are China III (29.64 %), III (18.03 %), III (22.81 %), III (42.16 %) and V (22.28 %) HDTs, respectively. The gridded vehicular emissions vary significantly, with emissions of CO, HC, CH₄ and CO₂ being mainly concentrated in central urban areas of cities (e.g., Beijing, Tangshan, Zhengzhou, Tianjin, Qingdao, Jinan). Nevertheless, the grids with high vehicular emissions of NO_X, PM₁₀, PM_2.5, BC and N₂O are mainly distributed along the expressway and the suburban roads of cities (e.g., Linyi, Tangshan, Jining, Weifang, Shijiazhuang, Tianjin, Baoding). Finally, multi-year uncertainties of vehicular emission inventory are discussed.

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin

The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.