The different sensitivities of aerosol optical properties to particle concentration, humidity, and hygroscopicity between the surface level and the upper boundary layer in Guangzhou, China.

This study investigates the impact of aerosol liquid water content (ALWC) and related factors, i.e., relative humidity (RH), aerosol mass concentration (PM2.5), and aerosol hygroscopicity, on aerosol optical properties, based on field measurements made in the Pearl River Delta (PRD) region of China at the surface (1 November 2019 to 21 January 2020) and in the upper boundary layer (the 532-m Guangzhou tower from 1 February to 21 March 2020). In general, temporal variations in the ambient aerosol backscattering coefficient (ßp) and ALWC followed each other. However, the surface ßp and 532-m ßp had generally opposite diurnal variation patterns, caused by dramatic differences in PM2.5 and ambient RH between the surface and the upper boundary layer. The ambient 532-m RH was systematically higher than the surface RH, with the latter having a much pronounced diurnal cycle than the former. The surface PM2.5 concentration was systematically higher than the PM2.5 concentration at 532 m, and their diurnal cycle patterns were overall opposite. These dramatic differences reveal that the atmospheric variables, i.e., ambient RH and the PM2.5 concentration in the upper boundary layer, cannot be directly represented by the same variables at the surface. Vertical variability should be considered. Clear differences in the sensitivities of aerosol light scattering to ambient RH, PM2.5, and aerosol hygroscopicity between the two levels were found and examined. Aerosol chemical composition played a minor role in causing the differences between the two levels. In particular, ßp was more sensitive to PM2.5 at the surface level but more to the ambient RH in the upper boundary layer. The larger contribution of aerosol loading to the variability in ßp at the surface implies that local emission controls can decrease ßp and further improve atmospheric visibility effectively at the surface during winter in the PRD region.

Contrasting Trends of Surface PM2.5, O3, and NO2 and Their Relationships with Meteorological Parameters in Typical Coastal and Inland Cities in the Yangtze River Delta.

The contrasting trends of surface particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and their relationships with meteorological parameters from 2015 to 2019 were investigated in the coastal city of Shanghai (SH) and the inland city of Hefei (HF), located in the Yangtze River Delta (YRD). In both cities, PM2.5 declined substantially, while O3 and NO2 showed peak values during 2017 when the most frequent extreme high-temperature events occurred. Wind speed was correlated most negatively with PM2.5 and NO2 concentrations, while surface temperature and relative humidity were most closely related to O3. All of the studied pollutants were reduced by rainfall scavenging, with the greatest reduction seen in PM2.5, followed by NO2 and O3. By contrast, air pollutants in the two cities were moderately strongly correlated, although PM2.5 concentrations were much lower and Ox (O3 + NO2) concentrations were higher in SH. Additionally, complex air pollution hours occurred more frequently in SH. Air pollutant concentrations changed more with wind direction in SH. A more effective washout effect was observed in HF, likely due to the more frequent strong convection and thunderstorms in inland areas. This research suggests pertinent air quality control measures should be designed accordingly for specific geographical locations.

The impact of the atmospheric turbulence-development tendency on new particle formation: a common finding on three continents.

A new mechanism of new particle formation (NPF) is investigated using comprehensive measurements of aerosol physicochemical quantities and meteorological variables made in three continents, including Beijing, China; the Southern Great Plains site in the USA; and SMEAR II Station in Hyytiälä, Finland. Despite the considerably different emissions of chemical species among the sites, a common relationship was found between the characteristics of NPF and the stability intensity. The stability parameter (ζ = Z/L, where Z is the height above ground and L is the Monin-Obukhov length) is found to play an important role; it drops significantly before NPF as the atmosphere becomes more unstable, which may serve as an indicator of nucleation bursts. As the atmosphere becomes unstable, the NPF duration is closely related to the tendency for turbulence development, which influences the evolution of the condensation sink. Presumably, the unstable atmosphere may dilute pre-existing particles, effectively reducing the condensation sink, especially at coarse mode to foster nucleation. This new mechanism is confirmed by model simulations using a molecular dynamic model that mimics the impact of turbulence development on nucleation by inducing and intensifying homogeneous nucleation events.

New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals.

The space-borne measured fine-mode aerosol optical depth (fAOD) is a gross index of column-integrated anthropogenic particulate pollutants, especially over the populated land. The fAOD is the product of the AOD and the fine-mode fraction (FMF). While there exist numerous global AOD products derived from many different satellite sensors, there have been much fewer, if any, global FMF products with a quality good enough to understand their spatiotemporal variations. This is key to understanding the global distribution and spatiotemporal variations of air pollutants, as well as their impacts on global environmental and climate changes. Modifying our newly developed retrieval algorithm to the latest global-scale Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product (Collection 6.1), a global 10-year FMF product is generated and analyzed here. We first validate the product through comparisons with the FMF derived from Aerosol Robotic Network (AERONET) measurements. Among our 169,313 samples, the satellite-derived FMFs agreed with the AERONET spectral deconvolution algorithm (SDA)-retrieved FMFs with a root-mean-square error (RMSE) of 0.22. Analyzed using this new product are the global patterns and interannual and seasonal variations of the FMF over land. In general, the FMF is large (>0.80) over Mexico, Myanmar, Laos, southern China, and Africa and less than 0.5 in the Sahelian and Sudanian zones of northern Africa. Seasonally, higher FMF values occur in summer and autumn. The linear trend in the satellite-derived and AERONET FMFs for different countries was explored. The upward trend in the FMFs was particularly strong over Australia since 2008. This study provides a new global view of changes in FMFs using a new satellite product that could help improve our understanding of air pollution around the world.

The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China.

Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) µg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 µg/m3 per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.

Satellite-Derived 1-km-Resolution PM1 Concentrations from 2014 to 2018 across China.

Particulate matter with aerodynamic diameters ≤1 µm (PM1) has a greater impact on the human health but has been less studied due to fewer ground observations. This study attempts to improve the retrieval accuracy and spatial resolution of satellite-based PM1 estimates using the new ground-based monitoring network in China. Therefore, a space-time extremely randomized trees (STET) model is first developed to estimate PM1 concentrations at a 1 km spatial resolution from 2014 to 2018 across mainland China. The STET model can derive daily PM1 concentrations with an average across-validation coefficient of determination of 0.77, a low root-mean-square error of 14.6 µg/m3, and a mean absolute error of 8.9 µg/m3. PM1 concentrations are generally low in most areas of China, except for the North China Plain and Sichuan Basin where intense human activities and poor natural conditions are prevalent, especially in winter. Moreover, PM1 pollution has greatly decreased over the past 5 years, benefiting from emission control in China. The STET model, incorporating the spatiotemporal information, shows superior performance in PM1 estimates relative to previous studies. This high-resolution and high-quality PM1 data set in China (i.e., ChinaHighPM1) can be greatly useful for air pollution studies in medium- or small-scale areas.