We present the mapping at fine spatial scale of aerosol optical properties using a mobile laboratory equipped with LIDAR (Light Detection And Ranging), sun photometer and in situ instruments for performing on-road measurements. The mobile campaign was conducted from 9 May to 19 May 2017 and had the main objective of mapping the distribution of pollutants in the Beijing and North China Plain (NCP) region. The highest AOD (Aerosol Optical Depth) at 440 nm of 1.34 and 1.9 were recorded during two heavy pollution episodes on 18 May and 19 May 2017, respectively. The lowest Planetary Boundary Layer (PBL) heights (0.5–1.5 km) were recorded during the heavy pollution events, correlating with the highest AOD and southern winds. The transport of desert dust from the Gobi Desert was captured during the mobile measurements, impacting Beijing during 9–13 May 2017. Exploring the NCP outside Beijing provided datasets for regions with scarce ground measurements and allowed the mapping of high aerosol concentrations when passing polluted cities in the NCP (Baoding, Tianjin and Tangshan) and along the Binhai New Area. For the first time, we provide mass concentration profiles from the synergy of LIDAR, sun photometer and in situ measurements. The case study along the Binhai New Area revealed mean extinction coefficients of 0.14 ± 0.10 km⁻¹ at 532 nm and a mass concentration of 80 ± 62 μg/m³ in the PBL (<2 km). The highest extinction (0.56 km⁻¹) and mass concentrations (404 μg/m³) were found in the industrial Binhai New Area. The PM₁₀ and PM_2.5 fractions of the total mass concentration profiles were separated using the columnar size distribution, derived from the sun photometer measurements. This study offers unique mobile datasets of the aerosol optical properties in the NCP for future applications, such as satellite validation and air quality studies.

Clear-sky detection (CSD) is of critical importance in solar energy applications and surface radiative budget studies. Existing CSD methods are not sufficiently validated due to the lack of high-temporal resolution and long-term CSD ground observations, especially at polluted sites. Using five-year high resolution ground-based solar radiation data and visual inspected Total Sky Imager (TSI) measurements at polluted Xianghe, a suburban site, this study validated 17 existing CSD methods and developed a new CSD model based on a machine-learning algorithm (Random Forest: RF). The propagation of systematic errors from input data to the calculated global horizontal irradiance (GHI) is confirmed with Mean Absolute Error (MAE) increased by 99.7% (from 20.00 to 39.93 W·m⁻²). Through qualitative evaluation, the novel Bright-Sun method outperforms the other traditional CSD methods at Xianghe site, with high accuracy score 0.73 and 0.92 under clear and cloudy conditions, respectively. The RF CSD model developed by one-year irradiance and TSI data shows more robust performance, with clear/cloudy-sky accuracy score of 0.78/0.88. Overall, the Bright-Sun and RF CSD models perform satisfactorily at heavy polluted sites. Further analysis shows the RF CSD model built with only GHI-related parameters can still achieve a mean accuracy score of 0.81, which indicates RF CSD models have the potential in dealing with sites only providing GHI observations.

Downward shortwave radiation (DSR) to the Earth’s surface is an essential renewable energy component. Accurate knowledge of solar radiation, i.e., solar energy resource assessment, is a prior requirement for the development of the solar energy industry. In the framework of solar resource assessment, site adaptation refers to leveraging short-term, high-quality ground-based observations as unbiased references to correct long-term, site-specific gridded model datasets, which has been playing an important role in this research area. This study evaluates 12 probabilistic site adaptation (PSA) methods for the correction of the hourly DSR data from multiple gridded DSR products in the Western Sichuan Plateau (WSP). Surface pyranometer observations are used as the reference to adapt predictions from two satellite products and two reanalysis products, collectively. Systematic quantification reveals inherent errors with root mean square errors (RMSEs) > 200 W/m² across all datasets. Through a comparative evaluation of three methodological categories (benchmarking, parametric/non-parametric, and quantile combination approaches), it is demonstrated that quantile-based ensemble methods achieve superior performance. The median ensemble (MED) method delivers optimal error reduction (RMSE: 163.97 W/m², nRMSE: 34.43%). The resulting optimal dataset, with a temporal resolution of 1 h and a spatial resolution of 0.05° × 0.05°, identifies the WSP as a region of exceptional energy potential, characterized by substantial annual total solar radiation (1593.10 kWh/m²/yr) and a stable temporal distribution (negative correlation between the total solar radiation and the coefficient of variation). This methodological framework provides actionable insights for solar resource optimization in complex terrains.

Aerosols play a crucial role in the surface radiative budget by absorbing and scattering both shortwave and longwave radiation. While most aerosol types exhibit a relatively minor longwave radiative forcing when compared to their shortwave counterparts, dust aerosols stand out for their substantial longwave radiative forcing. In this study, radiometers, a sun photometer, a microwave radiometer and the parameterization scheme for clear-sky radiation estimation were integrated to investigate the radiative properties of aerosols. During an event in Xianghe, North China Plain, from 25 April to 27 April 2018, both the composition (anthropogenic aerosol and dust) and the aerosol optical depth (AOD, ranging from 0.3 to 1.5) changed considerably. A notable shortwave aerosol radiative effect (SARE) was revealed by the integrated system (reaching its peak at −131.27 W·m⁻² on 26 April 2018), which was primarily attributed to a reduction in direct irradiance caused by anthropogenic aerosols. The SARE became relatively consistent over the three days as the AODs approached similar levels. Conversely, the longwave aerosol radiative effect (LARE) on the dust days ranged from 8.94 to 32.93 W·m⁻², significantly surpassing the values measured during the days of anthropogenic aerosol pollution, which ranged from 0.35 to 28.67 W·m⁻², despite lower AOD values. The LARE increased with a higher AOD and a lower Ångström exponent (AE), with a lower AE having a more pronounced impact on the LARE than a higher AOD. It was estimated that, on a daily basis, the LARE will offset approximately 25% of the SARE during dust events and during periods of heavy anthropogenic pollution.

An unmanned aerial vehicle (UAV) observation platform obtained the first vertical profiles of particle number size distribution (PNSD) from 7 to 16 July 2022 on the eastern slope of the Tibetan Plateau (ESTP). The results were from two flanks at the Chuni (CN) and Tianquan (TQ) sites, which are alongside a mountain (Mt. Erlang). The observations revealed a significant negative correlation between the planetary boundary layer height (PBLH) and the particle number concentration (PNC), and the correlation coefficient was −0.19. During the morning, the rise in the PBLH at the CN and TQ sites caused decreases of 16.43% and 58.76%, respectively, in the PNC. Three distinct profile characteristics were classified: Type I, the explosive growth of fine particles with a size range of 130–272 nm under conditions of low humidity, strong wind shear, and northerly winds; Type II, the process of particles with a size range of 130–272 nm showing hygroscopic growth into larger particles (e.g., 226–272 nm) under high humidity conditions (RH > 85%), with a maximum vertical change rate of about −1653 # cm⁻³ km⁻¹ for N_130–272 and about 3098 # cm⁻³ km⁻¹ for N_272–570; and Type III, in which during the occurrence of a surface low-pressure center and an 850 hPa low-vortex circulation in the Sichuan Basin, polluting air masses originating from urban agglomeration were transported to the ESTP region, resulting in an observed increase in the PNC below 600 nm. Overall, this study sheds light on the various factors affecting the vertical profiles of PNSD in the ESTP region, including regional transport, meteorological conditions, and particle growth processes, helping us to further understand the various features of the aerosol and atmospheric physical character in this key region.

Urban air pollution exhibits pronounced spatial heterogeneity, yet conventional fixed-site monitoring often cannot resolve fine-scale hotspot patterns. To address this issue, this study conducted a winter intensive observation campaign combining mobile measurements and synchronous fixed-site observations in Chengdu. The mobile observation was used to characterize the spatial distribution of particulate pollution, while fixed-site pollutant and meteorological data were used to provide temporal and background context. Three mobile observation sessions were performed each day at fixed local times (09:00–11:00, 14:00–16:00, and 19:00–21:00). Based on the PM_2.5 concentration, the observation period was categorized into two episodes: polluted episodes (PM_2.5 > 75 μg m⁻³) and clean episodes (<75 μg m⁻³). Polluted episodes were characterized by substantially elevated PM_2.5, PM₁₀, NOx, CO, and particle number concentrations, together with relatively weak wind speed, indicating enhanced accumulation under stagnant conditions. In contrast, clean episodes generally occurred under stronger ventilation and lower pollutant levels. The results revealed marked small-scale spatial variability and distinct temporal changes in particulate pollution. PCA was suitable for the dataset (Kaiser–Meyer–Olkin = 0.788; Bartlett’s test, p < 0.001), and the first three principal components explained 82.7% of the total variance. Cluster analysis further identified three pollution regimes among 224 samples: clean/ventilated (34.4%), intermediate accumulation (39.7%), and heavy accumulation (25.9%). These findings demonstrate that short-term intensive mobile monitoring can serve as a cost-effective supplement to conventional monitoring for hotspot identification and targeted urban air-pollution management.