Elucidating the relationship between aerosol concentration and summertime boundary layer structure in central China

Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method

Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS-Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T-mode principal component analysis (T-PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m-3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m-3 and 0.96 μg m-3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %-74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %-49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.

An analysis of atmospheric stability indices and parameters under air pollution conditions

The stability of the atmosphere plays an important role in changes in air pollutant concentrations. Stable atmospheric conditions cause pollutant concentrations to reach high values, which degrades the air quality in a particular region. This study aims to reveal the relationship between atmospheric stability indices/parameters (thermodynamic indices) and changes in air pollutant concentrations. Pollutant concentrations of PM10, PM2.5, SO2, NO2, CO, and O3 were statistically analyzed for a 10-year (2013-2022) period for nine air quality stations located in the megacity Istanbul. Based on national and international air quality standards, 145 episode days were determined for the days when these parameters exceeded the threshold values. Five stability indices (Showalter Index - SI, Lifted Index - LI, Severe Weather Index - SWEAT, K Index - KI, Totals Totals Index - TTI), and three stability parameters (Convective Available Potential Energy - CAPE, Convective Inhibition - CIN, Bulk Richardson Number - BRN) were used to determine the stability of the atmosphere for episode days. It has been found that in cases where air pollutant concentrations are high, the stability parameters reveal the stability of the atmosphere better than the stability indices. It was also found that there was at least one vertical inversion layer on 122 of the 145 episode days, these layers mostly (84%) occurred between the surface and 850 hPa levels, and the layer thicknesses were mostly between 0-250 m (84%).

Relationships between Springtime PM2.5, PM10, and O3 Pollution and the Boundary Layer Structure in Beijing, China

Complex pollution with high aerosol and ozone concentrations has recently been occurring in several densely populated cities in China, raising concerns about the influence of meteorological factors, including synoptic circulation and local conditions. In this study, comprehensive analyses on the associations between PM2.5, PM10, and O3 and meteorological conditions were conducted based on observations from radar wind profiler, microwave radiometer, automatic weather station, and air quality monitoring sites in Beijing during the spring of 2019. The results showed that the boundary layer height and temperature inversion were negatively (positively) correlated with PM (O3) concentrations, modulating the degree of air pollution. Five identified synoptic patterns were derived using geopotential height data of the ERA5 reanalysis, among which Type 1, characterised by south-westerly prevailing winds with high pressure to the south, was considered to be associated with severe PM and O3 contamination. This indicates that air pollutants originating from southern regions exert a major influence on Beijing through the transportation effect. In addition, high temperature, relative humidity, and low wind velocity exacerbate pollution. Overall, this study provides significant information for understanding the vital roles played by meteorological elements at both the regional and local scales in regulating air contamination during spring in Beijing.

Observations by Ground-Based MAX-DOAS of the Vertical Characters of Winter Pollution and the Influencing Factors of HONO Generation in Shanghai, China

Analyzing vertical distribution characters of air pollutants is conducive to study the mechanisms under polluted atmospheric conditions. Nitrous acid (HONO) is a kind of crucial species in photochemical cycles. Exploring the influence and sources of HONO in air pollution at different altitudes offers some insights into the research of tropospheric oxidation chemistry processes. Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements were conducted in Shanghai, China, from December 2017 to March 2018 to investigate vertical distributions and diurnal variations of trace gases (NO2, HONO, HCHO, SO2, and water vapor) and aerosol extinction coefficient in the boundary layer. Aerosol and NO2 showed decreasing profile exponentially, SO2 and HCHO concentrations were observed relatively high values in the middle layer. SO2 was caused by industrial emissions, while HCHO was from secondary sources. As for HONO, below 0.82 km, the heterogeneous reactions of NO2 impacted on forming HONO, while in the upper layers, vertical diffusion might be the dominant source. The contribution of OH production from HONO photolysis at different altitudes was mainly controlled by the concentration of HONO. MAX-DOAS measurements characterize the vertical structure of air pollutants in Shanghai and provide further understanding for HONO formation, which can help deploy advanced measurement platforms of regional air pollution over eastern China.

Characteristics of Chemical Speciation in PM1 in Six Representative Regions in China

A better knowledge of aerosol properties is of great significance for elucidating the complex mechanisms behind frequently occurring haze pollution events. In this study, we examine the temporal and spatial variations in both PM₁ and its major chemical constituents using three-year field measurements that were collected in six representative regions in China between 2012 and 2014. Our results show that both PM₁ and its chemical compositions varied significantly in space and time, with high PM₁ loadings mainly observed in the winter. By comparing chemical constituents between clean and polluted episodes, we find that the elevated PM₁ mass concentration during pollution events should be largely attributable to significant increases in organic matter (OM) and inorganic aerosols like sulfate, nitrate, and ammonium (SNA), indicative of the critical role of primary emissions and secondary aerosols in elevating PM₁ pollution levels. The ratios of PM₁/PM_2.5 are found to be generally high in Shanghai and Guangzhou, while relatively low ratios are seen in Xi'an and Chengdu, indicating anthropogenic emissions were more likely to accumulate in forms of finer particles. With respect to the relative importance of chemical components and meteorological factors quantified via statistical modeling practices, we find that primary emissions and secondary aerosols were the two leading factors contributing to PM₁ variations, though meteorological factors also played important roles in regulating the dispersion of atmospheric PM.

Assessing the pollutant evolution mechanisms of heavy pollution episodes in the Yangtze-Huaihe valley: A multiscale perspective

The Yangtze-Huaihe (YH) region experiences heavy aerosol pollution, characterized by high PM2.5 concentration. To unravel the pollutant evolution mechanism during the heavy pollution episodes (HPEs), this study combined observational data analysis and three-dimensional WRF-Chem simulations. From December 2, 2016 to January 15, 2017, YH region experienced 4 HPEs under the control by synoptic system, normally associated with a transport stage (TS) and a cumulative stage (CS). During the TS, pollutants are transported to the north of YH region through the near-surface, and then transported to the "mountain corridor" through the residual layer (RL) under the influence of prevailing wind. For the RL transport mechanism, the change of pollutant concentration cannot only consider the net flux in the horizontal direction, but also the role of the vertical movement is extremely important and cannot be ignored. By analyzing the mass conservation equation of pollutant, the results show that the advection transport and turbulent diffusion have a synergistic effect on the change of pollutant in the CS of three HPEs. The change of turbulence characteristics also affected by topography. For the "mountain corridors", which is accompanied by variable wind direction and turbulence diffusion is easily affected by wind shear. In addition, the turbulence characteristics are different during the TS and CS, especially the strong stable conditions in the CS at nighttime. The turbulence is intermittent, and the model has insufficient performance for turbulence, which will lead to differences for the simulation of pollutant concentration. In short, as the PM2.5 concentration linearly increases, the friction velocity (turbulent diffusion coefficient) decreases 63% (80%), 61% (78%) and 45% (68%), respectively. Therefore, the change of pollutants is less sensitive to the change of turbulence during the HPEs. The contribution of regional transport (local emissions) reaches 43% (47%), thus we need pay attention to the contribution of each part during the HPEs, which will help us to build a certain foundation for the emission reduction work in the future.

Spatio-temporal distribution of six pollutants and potential sources in the Hexi Corridor, Northwest China

Particulate matter (PM) concentrations are affected by anthropogenic emissions and sand transport jointly; however, the relative contributions from those two aspects are usually unknown. In our work, statistical analysis and back trajectories model were used to identify the dominant source in such area, by taking Yumen City as an example. We come to the conclusion that local emissions dominate the concentration of airborne pollutants, while sand transport plays a significant role on PM concentration. The conclusions were supported by the following results. (1) PM monthly mean concentrations at the two air quality stations, which are 70 km far away from each other, have the similar levels and variation trend; furthermore, a regression analysis of PM_2.5 and PM₁₀ daily concentrations between both stations indicated a significant correlation, suggesting that PM at both locations was influenced by the same emission sources; (2) statistical analysis results revealed that PM concentration has a positive correlation with wind speed, indicating the wind-blown dust and sand contribute mainly on PM concentration; (3) back-trajectory clustering analysis indicates that long-distance transport particulates from dust sources and their pathways had a significant impact on local PM concentrations.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China

Vertical wind shear (VWS) is one of the key meteorological factors in modulating ground-level particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5). Due to the lack of high-resolution vertical wind measurements, how the VWS affects ground-level PM2.5 remains highly debated. Here we employed the wind profiling observations from the fine-time-resolution radar wind profiler (RWP), together with hourly ground-level PM2.5 measurements, to explore the wind features in the planetary boundary layer (PBL) and their association with aerosols in Beijing for the period from December 1, 2018, to February 28, 2019. Overall, southerly wind anomalies almost dominated throughout the whole PBL or even beyond the PBL under polluted conditions during the course of a day, as totally opposed to the northerly wind anomalies in the PBL under clean conditions. Besides, the ground-level PM2.5 pollution exhibited a strong dependence on the VWS. A much weaker VWS was observed in the lower part of the PBL under polluted conditions, compared with that under clean conditions, which could be due to the strong ground-level PM2.5 accumulation induced by weak vertical mixing in the PBL. Notably, weak northbound transboundary PM2.5 pollution mainly appeared within the PBL, where relatively small VWS dominated. Above the PBL, strong northerlies winds also favored the long-range transport of aerosols, which in turn deteriorated the air quality in Beijing as well. This was well corroborated by the synoptic-scale circulation and backward trajectory analysis. Therefore, we argued here that not only the wind speed in the vertical but the VWS were important for the investigation of aerosol pollution formation mechanism in Beijing. Also, our findings offer wider insights into the role of VWS from RWP in modulating the variation of PM2.5, which deserves explicit consideration in the forecast of air quality in the future.

Synoptic pattern and planetary boundary layer structure associated with aerosol pollution during winter in Beijing, China

The day-to-day variations in the planetary boundary layer (PBL) structure and air quality are closely governed by large-scale synoptic forcings. Partly due to the lack of long-term PBL observations during the winter in Beijing, the complex relationships between the large-scale synoptic patterns, local PBL structures/processes, and PM_2.5 pollution have not been fully understood. Thus, this study systematically investigated these linkages by combining aerosol measurements, surface meteorological observations, radiosonde data, reanalysis, long-term three-dimensional meteorological simulations, and idealized meteorology-chemistry coupled simulations. Based on the validated long-term simulation results, the boundary layer height (BLH) in Beijing during two winters from 2013 to 2015 was calculated and compared with PM_2.5 measurements. A significant anti-correlation was found between the daily BLH and PM_2.5 concentration in Beijing, indicating the importance of the PBL structure on the variations in the aerosol pollution levels. Those days with low BLHs are often accompanied by a strong elevated thermal inversion layer. Based on the daily 900-hPa geopotential height fields, seven synoptic patterns were identified using an objective approach, in which two types were found to be associated with heavy PM_2.5 pollution in Beijing. One pattern was characterized by weak northwesterly prevailing winds and a strong elevated thermal inversion layer over Beijing, and the local emissions of aerosols played a decisive role in the formation of heavy pollution. The other pattern was associated with southerly prevailing winds, which could transport the pollutants emitted from southern cities to Beijing. According to the meteorology-chemistry coupled simulations, southerly regional transportation can contribute approximately 56% of the PM_2.5 in Beijing. The results of this study have important implications for understanding the crucial roles that multiscale meteorological factors play in modulating the aerosol pollution in Beijing during the winter.

Diagnosing atmospheric stability effects on the modeling accuracy of PM2.5 /AOD relationship in eastern China using radiosonde data

Atmospheric stability significantly influences the accumulation and dispersion of air pollutants in the near-surface atmosphere, yet few stability metrics have been applied as predictors in statistical PM_2.5 concentration mapping practices. In this study, eleven stability metrics were derived from radiosonde soundings collected in eastern China for the time period of 2015-2018 and then applied as independent predictors to explore their potential in favoring the prediction of PM_2.5. The statistical results show that the in situ PM_2.5 concentration measurements correlated well with these stability metrics, especially at monthly and seasonal timescales. In contrast, correlations at the daily timescale differed markedly between stability metric and also varied with seasons. Nevertheless, the modeling results indicate that incorporating these stability metrics into the PM_2.5 modeling framework rendered small contribution to PM_2.5 prediction accuracy, yielding an increase of R² by < 5% and a reduction of RMSE by < 1 μg/m³ on average. Compared with other stability indices, the inversion depth and intensity appeared to have relative larger benefiting potential. In general, our findings indicate that including these stability metrics would not result in significant contribution to the PM_2.5 prediction accuracy in eastern China since their effects could be partially overwhelmed or offset by other predictors such as AOD and boundary layer height.

Influence of Boundary Layer Structure and Low-Level Jet on PM2.5 Pollution in Beijing: A Case Study

Beijing experiences frequent PM_2.5 pollution, which is influenced by the planetary boundary layer (PBL) structure/process. Partly due to a lack of appropriate observations, the impacts of PBL on PM_2.5 pollution are not yet fully understood. Combining wind-profiler data, radiosonde measurements, near-surface meteorological observations, aerosol measurements, and three-dimensional simulations, this study investigated the influence of PBL structure and the low-level jet (LLJ) on the pollution in Beijing from 19 to 20 September 2015. The evolution of the LLJ was generally well simulated by the model, although the wind speed within the PBL was overestimated. Being influenced by the large-scale southerly prevailing winds, the aerosols emitted from the southern polluted regions could be easily transported to Beijing, contributing to ~68% of the PM_2.5 measured in Beijing on 20 September. The relative contribution of external transport of PM_2.5 to Beijing was high in the afternoon (≥80%), which was related to the strong southerly PBL winds and the presence of thermally-induced upslope winds. On 20 September, the LLJ in Beijing demonstrated a prominent diurnal variation, which was predominant in the morning and after sunset. The occurrence of the LLJ could enhance the dilution capacity in Beijing to some extent, which favors the dilution of pollutants at a local scale. This study has important implications for better understanding the complexity of PBL structure/process associated with PM_2.5 pollution in Beijing.

Characterization and source apportionment of marine aerosols over the East China Sea

Awareness of the importance of marine atmosphere for accurately estimating global aerosol budget and climate impacts has arisen recently. However, studies are limited due to the difficulty and inconvenience in sampling as well as the diversity of sources. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to investigate the fine particulate matter (PM_2.5) and its chemical components over the East China Sea (ECS) and offshore regions. In spite of slight under-predictions, model predictions agree well with observations over the ECS and along the coast. PM_2.5 and its major components in the mainland are higher than in marine area, suggesting Asian continent is a major emitter of marine aerosols. PM_2.5 and its components in marine regions show higher abundance during daytime than nighttime, while it is opposite in continental regions. Aerosol phase SO₄^2- is the most abundant component of PM_2.5 over the ECS with an average concentration of 5.12 μg m^-3, followed by NH₄⁺ (1.02 μg m^-3) and primary organic aerosol (POA) (0.92 μg m^-3). Industry and ship emissions are the top two contributors to primary (PPM) and total PM_2.5 over the ECS, while industry and agriculture sectors are major sources for secondary inorganic aerosols (SIA), followed by ship emissions. For terrestrial regions, industry and agriculture are predominant sources of PM_2.5 and SIA, while industry and residential activities are the top two contributors to PPM. This study improves the understanding of transport and accumulation of air pollutants over the ECS and adjacent regions, and provides useful information for designing efficient control strategies.