Near-surface PM2 Research Articles

Multi-Scale Meteorological Impact on PM2.5 Pollution in Tangshan, Northern China.

Tangshan, a major industrial and agricultural center in northern China, frequently experiences significant PM2.5 pollution events during winter, impacting its large population. These pollution episodes are influenced by multi-scale meteorological processes, though the complex mechanisms remain not fully understood. This study integrates surface PM2.5 concentration data, ground-based and upper-air meteorological observations, and ERA5 reanalysis data from 2015 to 2019 to explore the interactions between local planetary boundary layer (PBL) structures and large-scale atmospheric processes driving PM2.5 pollution in Tangshan. The results indicate that seasonal variations in PM2.5 pollution levels are closely linked to changes in PBL thermal stability. During winter, day-to-day increases in PM2.5 concentrations are often tied to atmospheric warming above 1500 m, as enhanced thermal inversions and reduced PBL heights lead to pollutant accumulation. Regionally, this aloft warming is driven by a high-pressure system at 850 hPa over the southern North China Plain, accompanied by prevailing southwesterly winds. Additionally, southwesterly winds within the PBL can transport pollutants from the adjacent Beijing-Tianjin-Hebei region to Tangshan, worsening pollution. Simulations from the chemical transport model indicate that regional pollutant transport can contribute to approximately half of the near-surface PM2.5 concentration under the unfavorable synoptic conditions. These findings underscore the importance of multi-scale meteorology in predicting and mitigating severe wintertime PM2.5 pollution in Tangshan and surrounding regions.

Expression of Concern: Heavy near-surface PM2.5 pollution in Lhasa, China during a relatively static winter period (Chemosphere 214 January 2019, Pages 314-318)

Expression of Concern: Heavy near-surface PM2.5 pollution in Lhasa, China during a relatively static winter period (Chemosphere 214 January 2019, Pages 314-318)

Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Near-surface PM2.5 estimates remain a global scientific research challenge due to their effect on human fitness and atmospheric environmental quality. However, practical near-surface PM2.5 estimates are impeded by the incomplete monitoring data. In this study, we propose the trigonometric polynomial fitting (TPF) method to estimate near-surface PM2.5 concentrations in south-central China during 2015. We employ 10-fold cross-validation (CV) to assess the reliability of TPF in estimating practical PM2.5 values. When compared to alternative methods such as the orthogonal polynomial fitting (OBF) method based on Chebyshev basis functions, Kriging interpolation, and radial basis function (RBF) interpolation, our results show that utilizing TPF31, with a maximum order of 3 in the x direction and a maximum order of 1 in the y direction, leads to superior efficiency through error minimization. TPF31 reduces MAE and RMSE by 1.93%, 24%, 6.96% and 3.6%, 23.07%, 10.43%, respectively, compared to the other three methods. In addition, the TPF31 method effectively reconstructs the spatial distribution of PM2.5 concentrations in the unevenly distributed observation stations of Inner Mongolia and the marginal regions of the study area. The reconstructed spatial distribution is remarkably smooth. Despite the non-uniform distribution of observation stations and the presence of missing data, the TPF31 method demonstrates exceptional effectiveness in accurately capturing the inherent physical attributes of spatial distribution. The theoretical and experimental results emphasize that the TPF method holds significant potential for accurately reconstructing the spatial distribution of PM2.5 in China.

Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020

The pursuit of higher-resolution and more reliable spatial distribution simulation results for air pollutants is important to human health and environmental safety. However, the lack of high-resolution remote sensing retrieval parameters for gaseous pollutants (sulfur dioxide and ozone) limits the simulation effect to a 1 km resolution. To address this issue, we sequentially generated and optimized the spatial distributions of near-surface PM2.5, SO2, and ozone at a 1 km resolution in China through two approaches. First, we employed spatial sampling, random ID, and parameter convolution methods to jointly optimize a tree-based machine-learning gradient-boosting framework, LightGBM, and improve the performance of spatial air pollutant simulations. Second, we simulated PM2.5, used the simulated PM2.5 result to simulate SO2, and then used the simulated SO2 to simulate ozone. We improved the stability of 1 km-resolution SO2 and ozone products through the proposed sequence of multiple-pollutant simulations. The cross-validation (CV) of the random sample yielded an R2 of 0.90 and an RMSE of 9.62 µg∙m−3 for PM2.5, an R2 of 0.92 and an RMSE of 3.9 µg∙m−3 for SO2, and an R2 of 0.94 and an RMSE of 5.9 µg∙m−3 for ozone, which are values better than those in previous related studies. In addition, we tested the reliability of PM2.5, SO2, and ozone products in China through spatial distribution reliability analysis and parameter importance reliability analysis. The PM2.5, SO2, and ozone simulation models and multiple-air-pollutant (MuAP) products generated by the two optimization methods proposed in this study are of great value for long-term, large-scale, and regional-scale air pollution monitoring and predictions, as well as population health assessments.

Pollutant-meteorological factors and cardio-respiratory mortality in Portugal: Seasonal variability and associations

Seasonal variations in cardiorespiratory diseases may be influenced by air pollution and meteorological factors. This work aims to highlight the relevance of a complete seasonal characterization of the pollutant-meteorological factors and cardio-respiratory mortality in Portugal and the relationships between health outcomes and environmental risk factors. To this end, air pollution and meteorological variables along with health outcomes were analyzed at national level and on a monthly basis for the period of 2011–2020. It was found that cardiorespiratory mortality rates during winter were 44% higher than during the summer. Furthermore, particulate matter with aerodynamic diameters of 10 and 2.5 μm (μm) or smaller (PM10 and PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2) showed a seasonal variability with the highest concentrations during winter while ozone (O3) presented higher concentrations during spring and summer. PM10, PM2.5 and NO2, showed a positive correlation between seasons, indicating similar patterns of behavior. Canonical correlation analysis (CCA) applied to pollutant-meteorological and cardiorespiratory mortality data indicates a strong linear correlation between pollutant-meteorological factors and health outcomes. The first canonical correlation was 0.889, and the second was 0.545, both statistically significant (p-value < 0.001). The CCA results suggest that there is a strong association between near-surface temperature, relative humidity, PM10, PM2.5, CO and NO2 and health outcomes. The results of this study provide important information of the seasonal variability of air pollutants and meteorological factors in Portugal and their associations with cardiorespiratory mortality.

Spatiotemporal Distribution Characteristics and Multi-Factor Analysis of Near-Surface PM2.5 Concentration in Local-Scale Urban Areas

Near-surface PM2.5 concentrations have been greatly exacerbated by urban land expansion and dense urban traffic. This study aims to clarify the effects of multiple factors on near-surface PM2.5 concentrations from three perspectives of background climatic variables, urban morphology variables, and traffic-related emission intensity. First, two case areas covering multiple local blocks were selected to conduct mobile measurements under different climatic conditions. The observed meteorological parameters and PM2.5 concentration were obtained through GIS-based imaging. These interpolation results of air temperature and relative humidity reveal highly spatiotemporal diversity, which is greatly influenced by artificial heat emissions and spatial morphology characteristics in local areas. The PM2.5 concentration on measurement days also varies considerably from the lowest value of 44~56 μg/m3 in October to about 500 μg/m3 in December in Harbin winter and ranges between about 5 μg/m3 and 50 μg/m3 in Guangzhou summer. The correlation analysis reveals that both the climatic conditions and urban morphology characteristics are significantly correlated with local PM2.5 concentration. Especially for Guangzhou summer, the PM2.5 concentration was positively correlated with the street traffic emission source intensity with correlation coefficient reaching about 0.79. Multivariate nonlinear formulas were applied to fit the association between these factors and PM2.5 concentration with higher determined coefficients. And optimization strategies are thus suggested to improve the urban air quality in local-scale areas. This attribution analysis contributes to environmentally friendly urban construction.

The impact of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5: A modeling case study in Northern China

The quantification of the effectiveness of anthropogenic emission control measures is crucial for future air quality policies. Meteorology plays a vital role in haze pollution, and the interactions between aerosol and meteorology have been widely studied. However, it is not fully clear how aerosol-meteorology feedback affects the effectiveness of emission reduction for PM2.5, which limits our ability of optimizing anti-pollution policies. Here, with the two-way atmospheric chemical transport model WRF-Chem, the effects of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5 during a winter severe haze event in 2016 over the Northern China Plain (NCP) are studied. In the more polluted area of NCP (MP_NCP) during the daytime, 20% emission reduction over NCP increases near-surface downward shortwave radiation by 4.62 W/m2, 2 m temperature by 0.08 °C, boundary layer height by 7.19 m and reduces 2 m relative humidity by 0.31% and thereby alleviates worsened meteorological conditions caused by aerosol effect. As a result, in MP_NCP, 20% emission reduction without aerosol-meteorology feedback leads to a decrease of 40.49μg/m3 of near-surface PM2.5 and the above meteorological changes decrease near-surface PM2.5 concentration by 7.82μg/m3, indicating that aerosol-meteorology feedback strengthens the effectiveness of emission reduction by 19%. In the less polluted area (LP_NCP), aerosol effect induced meteorological changes decrease PM2.5 concentration by 7.57μg/m3 and 20% emission reduction without aerosol-meteorology feedback leads to a decrease of 13.15μg/m3 in near-surface PM2.5. This reveals a remarkable enhancement of 58% in the effectiveness of emission reduction, which is much larger than that in MP_NCP. Such difference can be attributed to the presence of more clouds in LP_NCP, where the decrease in liquid water path, along with the increase in the planetary boundary layer height, jointly contributes to the PM2.5 decrease. Moreover, the effect of aerosol-meteorology feedback on the effectiveness of emission reduction for PM2.5 is nonlinear. With increasing PM2.5 concentration, the aerosol-meteorology feedback induced PM2.5 reduction first increases and then stabilizes once the PM2.5 concentration exceeds 350 μg m−3. This study can provide reference for air pollution control strategies.

Modeling Impacts of Urbanization on Winter Boundary Layer Meteorology and Aerosol Pollution in the Central Liaoning City Cluster, China.

The influence of urbanization on the frequent winter aerosol pollution events in Northeast China is not fully understood. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) coupled with urban canopy (UC) models was used to simulate the impact of urbanization on an aerosol pollution process in the Central Liaoning city cluster (CLCC), China. To investigate the main mechanisms of urban expansion and UC on the winter atmospheric environment and the atmospheric diffusion capacity (ADC) in the CLCC, three simulation cases were designed using land-use datasets from different periods and different UC schemes. A comparative analysis of the simulation results showed that the land-use change (LU) and both LU and UC (LUUC) effects lead to higher surface temperature and lower relative humidity and wind speed in the CLCC by decreasing surface albedo, increasing sensible heat flux, and increasing surface roughness, with a spatial distribution similar to the distribution of LU. The thermal effect leads to an increase in atmospheric instability, an increase in boundary layer height and diffusion coefficient, and an increase in the ADC. The LU and LUUC effects lead to a significant decrease in near-surface PM2.5 concentrations in the CLCC due to changes in meteorological conditions and ADC within the boundary layer. The reduction in surface PM2.5 concentrations due to the LU effect is stronger at night than during daytime, while the LUUC effect leads to a greater reduction in surface PM2.5 concentrations during the day, mainly due to stronger diffusion and dilution caused by the effect of urban turbulence within different levels caused by the more complex UC scheme. In this study, the LU and LUUC effects result in greater thermal than dynamic effects, and both have a negative impact on surface PM2.5 concentrations, but redistribute pollutants from the lower urban troposphere to higher altitudes.

The vertical pollution structure reflected by aerosol optical properties in Beijing and its relation with meteorological conditions in the autumn and winter of 2017–2020

The high temporal aerosol optical extinction vertical profiles observed by LiDAR in Beijing and its vicinity areas (BIV) in autumn and winter from October 2017 to February 2020 are investigated in this study. Through the clustering analysis using the machine learning, we separate and distinguish the clean conditions and aerosol pollution convergence situations at different altitudes, obtain six vertical aerosol pollution distribution patterns, and discuss the differences in the diurnal variation. Patterns with the entire clean layer are the typical category, accounting for 81.4% of the overall percentage. The patterns with pollution convergence at different heights at the top and above the boundary layer and in the lower and middle parts of the boundary layer account for 11.9% and 6.7%, respectively. The relationship among vertical pollution structure, the meteorological elements, and the near-surface PM2.5 mass concentration are discussed. Pollution convergences are mainly affected by northward aerosol pollution transport. Significant humidification and temperature inversion show aerosol pollution's influence on regional meteorological conditions. The heavy near-surface PM2.5 mass concentration is only related to the category with the aerosol pollution convergence in the lower and middle boundary layer when the high values of the PLAM index (parameters linking air quality to meteorological elements), which quantifies the regional air mass stability, and the boundary layer height (BLH) appear. Other aerosol pollution convergences above the boundary layer cannot reflect in the near-surface PM2.5 mass concentration. The pollution and associated meteorological conditions near the surface are more severe in the evening than in the morning. Regional unfavorable meteorological conditions contributed 47% and 62% to surface pollution in the morning and evening, respectively. Pollution transport and the two-way feedback jointly control the near-surface aerosol pollution in the morning; The unfavorable meteorological conditions may play a dominant role in the evening.

Attenuation of mountain-valley circulations on PM2.5 pollution over the western Sichuan basin, southwest China

The Sichuan Basin (SCB), located immediately to the east of the Tibetan Plateau (TP) in southwest China, is identified as a region with severe PM2.5 pollution, especially in the western SCB region. To understand the terrain effect on the atmospheric environment change in detail, this study investigated the effect degree and meteorological mechanism of thermally driven mountain-valley breeze (MVB) circulations on wintertime PM2.5 in the western SCB region, based on the near-surface observations of PM2.5 and the ERA5 reanalysis data of meteorology. The results showed that the western SCB edge exhibited a significant diurnal change of MVB, shifting between daytime upslope easterly flows and nighttime downslope westerly flows. The frequency of the MVB circulations was accounted for 39% days in December 2017, with the mountain and valley breeze-controlling periods being from 01:00 to 05:00 and 14:00 to 17:00 local time, respectively. Notably, the hourly PM2.5 reductions of 13.9 ± 4.6 μg m−3 was averaged during the MVB days in the western SCB edge, resulting in a decrease of 46.4 ± 14.0% in PM2.5 pollution with the MVB, which indicates that the MVB could alleviate PM2.5 pollution in improving air quality over the western SCB region. The daytime MVB circulations drove the transport of PM2.5-rich air mass in the atmospheric boundary layer from the polluted western SCB edge to the surrounding regions, causing 22% attenuation in near-surface PM2.5 concentrations during the valley breeze-controlling period. The nocturnal MVB circulations carried clean TP air eastward downslope along the eastern slope of the TP into the polluted western SCB region, mitigating PM2.5 levels by 20% during the mountain breeze-controlling period.

Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM2.5) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM2.5, monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM2.5; subsequent 10-fold cross validation (CV) yielded a CV R2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m−3. We investigated the spatial pattern of PM2.5 concentrations over multiple countries and seasonal variation in PM2.5 concentrations. Diurnal variation of a severe PM2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R2 of 0.60 and RMSE of 8.18 μg m−3. This study demonstrates the potential for satellite-based PM2.5 estimation for areas with insufficient measuring stations.

The impact of biomass burning emissions on aerosol concentrations and depositions in the northern South China Sea region

The impact of biomass burning (BB) emissions on concentrations and depositions of major aerosol components in the northern South China Sea (NSCS) region (105∼115°E and 15∼21°N) from December 2020 to April 2021 has been investigated using the WRF-chem model (weather research and forecasting model coupled with chemistry) in combination with aerosol composition measurement at a mountain site in the Hainan Island of China. Model comparisons with observations in Hainan demonstrated the effectiveness of WRF-chem in simulating aerosol components (PM2.5, black carbon (BC), organic matter (OM), sulfate, nitrate, and ammonium) in the NSCS region. The influences of BB emissions were significant on near-surface PM2.5, BC, and OM but nearly negligible on secondary inorganic components (i.e., sulfate, nitrate, and ammonium). The NSCS regional and period mean PM2.5, BC, and OM concentrations were 17.2 μg/m3, 0.65 μg/m3, and 6.7 μg/m3, in which 20%, 13%, and 35%, respectively, were from BB emissions. The mean PM2.5, BC, and OM deposition fluxes were 73.8 mg/m2/month, 2.9 mg/m2/month, and 29.3 mg/m2/month, respectively, and 22%, 15%, and 38% of these depositions were from BB emissions. Dry deposition dominated the removal of aerosols. For both aerosol concentrations and depositions, the influences of BB emissions exhibited evidently larger contributions in spring (March and April) than those in winter (December to February). Most of the BB emissions were from mainland Southeast Asia (MSEA), in terms of period mean, 79%, 72%, and 81% of the BB PM2.5, BB BC, and BB OM concentrations and 82%, 77%, and 83% of their depositions in the NSCS region were attributed to the MSEA region. The results of this study suggested that BB emissions have strong implications for air quality and biogeochemical cycle of the NSCS region.

Embedded information of aerosol type, hygroscopicity and scattering enhancement factor revealed by the relationship between PM2.5 and aerosol optical depth

Satellite aerosol optical depth (AOD) provides an alternative way to depict the spatial distribution of near-surface PM2.5. In this study, a mathematical formulation of how PM2.5 is related to AOD is presented. When simplified to a linear equation, a functional dependence of the slope on the aerosol type, scattering enhancement factor f(RH), and boundary layer height is revealed, while the influence of the vertical aerosol profile is embedded in the intercept. Specifically, we focus on the effects of aerosol properties and employ a new aerosol index (Normalized Gradient Aerosol Index, NGAI) for classifying aerosol subtypes. The combination of AOD difference at shorter wavelengths over longer-wavelength AOD from AERONET data could distinguish and subclassify aerosol types previously indistinguishable by AE (i.e., urban-industrial pollution, U/I, and biomass burning, BB). AOD-PM2.5 regressions are performed on these aerosol subtypes at various relative humidity (RH) levels. The results suggest that BB aerosols are nearly hydrophobic until the RH exceeds 80 %, while the AOD-PM2.5 regressions for U/I depend on RH levels. Moreover, the scattering enhancement factor f(RH) can be calculated by taking the ratio of intercepts between dry and humidity conditions, which is proposed and tested for the first time in this study. Our results show an f(RH ≥ 80 %) of ∼2.6 for U/I-dominated aerosols, whereas the value is not over 1.5 for BB aerosols. The f(RH) can be further used to derive the optical hygroscopicity parameter (κsca), demonstrating that the NGAI can be used to exploit differences in aerosol hygroscopicity and improve the AOD-PM2.5 relationship.

Evaluation and Comparison of Spatio-Temporal Relationship between Multiple Satellite Aerosol Optical Depth (AOD) and Near-Surface PM2.5 Concentration over China

Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016–2018 using the correlation coefficient (R), the PM2.5/AOD ratio (η), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R &gt; 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R &gt; 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (η &gt; 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q &gt; 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R &gt; 0.5, q &gt; 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China.

Optimization of Land Use Regression Modelling of PM2.5 Spatial Variations in Different Seasons across China

Fine particulate matter (PM2.5), one of the main components of haze, is of wide concern for its potential negative health effects. In order to further improve ambient air quality, it is essential to conclude the spatial variability of pollutants by investigating air pollution exposure. We divide China into two parts, north and south, and use a Land Use Regression (LUR) model to extract data including meteorological data, land use factors, and AOD retrievals, and use the machine learning algorithm to optimize the model to achieve predictions of the spatial distribution of near-surface PM2.5 mass concentrations in southern and northern China. We evaluated the seasonal consistency of the models in southern and northern China, and in northern China, we found a better fit with better seasonal consistency for the heating season and annual average model, while in southern China, we did not find a more fitted seasonal phase. The study illustrates that it is feasible to simulate the spatial distribution of PM2.5 mass concentration in large-scale areas based on the LUR model, and the seasonal consistency of the LUR model has been done to some extent.

Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China

Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM2.5 in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers of TIs (MultiTI) and their impacts on near-surface PM2.5 concentrations over eastern China that covers a range of topographies and climates, are systematically investigated based on global reanalysis ERA5 and the nationwide monitoring PM2.5 dataset from 2014 to 2020. TIs occurred mostly in the early morning. Different types of TIs present distinctive seasonal and spatial patterns. The majority of SBTIs and ElTIs occurred during nighttime in northern China and daytime in southern China, respectively, as the result of their formation mechanisms. SBTIs usually had larger TLR while ElTIs had deeper depth. SBTIs showed strong enhancement effects on PM2.5 concentration over the study domain while ElTIs showed more obvious impacts on northern nocturnal PM2.5. The peak time of PM2.5 was found around 18:00–22:00 LST, and TLR and depth of TIs are thought to be more relevant to PM2.5 peak concentration due to their coincident peak times. The strength of TIs is therefore more crucial in regulating PM2.5 than its occurrence frequency. Based on statistical analysis, our study provided a large picture of the generic spatiotemporal patterns of TIs and illustrated the impacts of different TIs on surface PM2.5 pollution on a diurnal basis. For a deeper understanding of the formation of PM2.5 pollution, more attention needs to be paid to the nocturnal PM2.5 not only at surface level but also at higher levels in the presence of TIs.

Why do extreme particulate pollution events occur in low-emission Yunnan Province, China?

Yunnan Province in southwest China has low anthropogenic emissions, but in recent years it encountered unprecedented serious particulate pollution. To clarify the causes of pollution in this province, this study comprehensively analyzed the spatial-temporal distribution of near-surface PM2.5 (particles with aerodynamic diameter less than 2.5 μm) from 2017 to 2021, which was retrieved from Himawari-8 aerosol optical depth. The results show that, particulate pollution mainly occurred in the dry season (November to April), especially in March and April. And the heavily polluted areas mainly distributed in the southwest of the province, where there is small population and high forest cover. Dust, polluted dust, and elevated smoke were the most spatio-temporally variable aerosols, which should be attributed to biomass combustion and polluted air mass transportation from neighboring countries. The comparisons between effects of anthropogenic emissions, stable weather and pollutant transportation on local air quality indicate that exogenous pollutants should be mainly responsible for the concentrated outbreak of particulate pollution in Yunnan Province during the second half of the dry season. Besides, the stable weather conditions would further aggravate the deterioration of air quality. This study provides important reference for further strengthening joint prevention and control of regional air pollution.

Direct and indirect effects and feedbacks of biomass burning aerosols over Mainland Southeast Asia and South China in springtime

Southeast Asia is one of the largest biomass burning (BB) source regions in the world. In order to promote our understanding of BB aerosol characteristics and environmental impacts, this study investigated the emission, composition, evolution, radiative effects, and feedbacks of BB aerosols from Mainland Southeast Asia during 15 March to 15 April 2019 by using an online-coupled regional chemistry/aerosol-climate model RIEMS-Chem. Model results are compared against a variety of ground and vertical observations, indicating a generally good model performance for meteorology, aerosol chemical compositions, and aerosol optical properties. It is found that BB aerosols contributed significantly to regional particulate matter (PM), accounting for up to 90 % of the near-surface PM2.5, BC, and OC concentrations over the BB source regions of north Mainland Southeast Asia and for approximately 30–70 % over wide downwind areas including most areas of southwest China and portions of south China. At the top of atmosphere (TOA), BB aerosols exerted a positive all-sky radiative effect (DREBB) up to 25 W/m2 over north Vietnam and south China, a negative DREBB up to −10 W/m2 over Myanmar, western Thailand, and southwest China. Meanwhile, the indirect radiative effect (IREBB) was consistently negative, with the maximum of −10 W/m2 over downwind areas with cloud coverage, e.g., from north Vietnam to most of south China. The subregional (95–125°E and 10–30°N) and period mean DREBB and IREBB at TOA were estimated to be 0.69 W/m2 and − 0.63 W/m2, respectively, leading a total radiative effect (TREBB) of 0.06 W/m2 at TOA. The radiative effects of BB aerosols led to decreases in sensible and latent heat fluxes, near-surface temperature, PBL height, and wind speed of 6.0 Wm−2, 9.0 Wm−2, 0.26 °C, 38.7 m, and 0.1 m/s, respectively, accompanied with an increase in RH of 1.9 %, averaged over the subregion and the study period. The accumulated precipitation during the study period was apparently reduced by BB aerosols from east Thailand to south China, with the maximum reduction up to 14 cm (exceeding 40 %) over north Vietnam and south China. TREBB tended to increase mean near-surface PM2.5 and its component concentrations, with the maximum percentage increase up to 24 % over the BB source regions of north Mainland Southeast Asia, resulting from the combined effects of dynamic and chemical feedbacks. DREBB generally dominated over IREBB in the feedback-induced PM2.5 concentration changes.

A New Coupling Method for PM2.5 Concentration Estimation by the Satellite-Based Semiempirical Model and Numerical Model

Aerosol optical and chemical properties play a major role in the retrieval of PM2.5 concentrations based on aerosol optical depth (AOD) data from satellites in the conventional semiempirical model (SEM). However, limited observation information hinders the high-resolution estimation of PM2.5. Therefore, a new method for evaluating near-surface PM2.5 at high spatial resolution is developed by coupling the SEM and the chemical transport model (CTM)-based numerical (CSEN) model. The numerical model can provide large-scale information for aerosol properties with high spatial resolution at a large scale based on emissions and meteorology, though it can still be biased in simulating absolute PM2.5 concentrations. Therefore, the two crucial aerosol characteristic parameters, including the coefficient integrated humidity effect (γ′) and the comprehensive reference value of aerosol properties (K) in SEM, have been redefined using the WRF-Chem numerical model. Improved model performance was observed for these results compared with the original SEM results. The monthly averaged correlation coefficients (R) by CSEN were 0.92, 0.82, 0.84, and 0.83 in January, April, July, and October, respectively, whereas those of the SEM were 0.80, 0.77, 0.72, and 0.72, respectively. All the statistical metrics of the model validation showed significant improvements in all seasons. The reduced biases of estimated PM2.5 by CSEN indicated the effect of hygroscopic growth and aerosol properties affected by the meteorology on the relationship between AOD and estimated PM2.5 concentrations, especially in winter and summer. The better performance of the CSEN model provides insight for air quality monitoring at different scales, which supplies important information for air pollution control policies and health impact analysis.

Estimating spatio-temporal variability of aerosol pollution in Yunnan Province, China

Generally, areas with more anthropogenic emissions are more polluted. However, Yunnan Province, located in the southwest of China, has experienced serious aerosol pollution in recent years despite overall low anthropogenic emissions and high vegetation coverage. To explore the spatio-temporal variability of aerosol pollution in Yunnan, this study retrieved the near-surface PM2.5 and PM10 from 2017 to 2020 using random forest method based on Himawari-8 AOD data, and the influences of meteorological factors and terrain are also considered. The results show that aerosol pollution in Yunnan Province has significant spatio-temporal variety. Specifically, the annual average PM2.5 and PM10 of the whole province are 39.16 μg/m3 and 68.87 μg/m3 respectively, which are mainly contributed by the particulate matter in dry season (November to April), especially that in March and April. And PM distribution here differs greatly from north to south with 25° N as the boundary in dry season. From November to January, pollution level in the northern part is higher than that in the southern, while it is the opposite from February to April, which is mainly affected by the regional transport of pollutant, rather than the changes of local emissions or meteorological conditions.