While atmospheric deposition plays a vital role in cleaning air pollutants, it also supplies toxic heavy metals and metalloids (MMs) to the receiving terrestrial and aquatic ecosystems and threatens human health through food chains. To characterize the input of atmospheric deposition to agricultural soils, bulk rain samples were collected on an event basis at a rural site in the North China Plain during 2016–2020. The results show that higher concentrations of MMs in bulk rain samples were associated with western and southern air masses passing polluted areas. In addition, the annual deposition flux of MMs tends to decline during the study period, coinciding with the inter-annual variations of particulate matter rather than the precipitation amounts. Of note, the deposition flux of MMs that exist entirely in fine particles declined significantly compared to those that exist in coarse particulate form, indicating that the clean air actions implemented in recent years were highly effective in reducing ambient MMs from anthropogenic emissions. The positive matrix factorization receptor model was also applied to the whole data set for bulk depositions and five sources were identified as agricultural (biomass burning and soil), dust, coal combustion, industrial and traffic emissions. These factors contributed 41%, 24%, 21%, 9% and 5% of the chemical components in bulk depositions, respectively. Future control strategies should tighten the emissions from combustion and soil/dust in the North China Plain to protect agriculture from atmospheric MMs depositions.

To investigate the seasonal characteristics, sources, and regional transport patterns of precipitation components in the high-elevation mountainous regions, field sampling was conducted at Mt. Heng (Hunan, South China) from June 2021 to May 2022. In total, 114 precipitation samples were collected and subjected to chemical analysis, including pH, major inorganic ions, and heavy metals. During the study period, the precipitation at Mt. Heng was generally weakly acidic. The concentrations of metals and acidic anions (NO₃⁻ and SO₄²⁻) were higher in the winter and lower in the summer, whereas the concentration of the primary neutralizing cation, NH₄⁺, peaked during the summer. An association was observed between precipitation pH and metal concentrations, whereby acidic precipitation samples exhibited marginally elevated metal concentrations overall. An additional analysis of winter precipitation chemistry at Mt. Heng revealed an increasing trend of ions from 2015 to 2018, followed by a decrease from 2019 to 2021. This trend coincided with the concentrations of NO₂ and SO₂ in the surrounding cities, reflecting the results of clean air actions. The results of the source analysis revealed five major sources: secondary sources (41.5%), coal combustion (24.7%), a mixed source of biomass burning and aged sea salt (11.6%), dust (10.8%), and industrial emissions (11.4%). Backward trajectory cluster analysis revealed that air masses originating from the northern regions were generally more polluted than those from the southern regions. This study provides fundamental data and scientific support for regional atmospheric pollution control and ecological protection in South China.

Water vapor transport is an important foundation and prerequisite for the occurrence of rainstorms. Consequently, the understanding of water vapor transport as well as the sources of water vapor during rainstorm processes should be considered as essential to study the formation mechanism of rainstorms. In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is adopted for backward tracking of water vapor transport trajectories and sources during the “7·20” extraordinary heavy rain process in Zhengzhou City of China that occurred on 20 July 2021. On this basis, the trajectory clustering method is applied to quantitatively analyze the contributions of water vapor sources, aiming to provide a basis for exploring the maintenance mechanism of this extreme rainstorm event. The spatio-temporal characteristics of this rainstorm event show that there are 4 consecutive days with the precipitation reaching or exceeding the rainstorm level across the whole Zhengzhou City, with the daily rainfall amounts at eight national meteorological stations all breaking their respective historical extreme values. The regional-averaged rainfall amount in Zhengzhou City is 527.4 mm, while the maximum accumulated rainfall amount reaches 985.2 mm at Xinmi station and the maximum hourly rainfall amount at Zhengzhou national meteorological station reaches 201.9 mm h⁻¹. The water vapor sources for this rainfall process, ranked in descending order of contribution, are the Western Pacific, inland areas of Northwest China and South China, and South China Sea. The water vapor at lower levels is mainly transported from the Western Pacific and the South China Sea, while those from the inland areas of Northwest China and South China provide a supply of water vapor at upper levels to a certain extent. The water vapor at 950 hPa is mainly sourced from the Western Pacific and South China Sea, accounting for 56% and 44%, respectively. The water vapor at 850 hPa mainly derives from the Western Pacific and the inland areas of South China, contributing 58% and 34% of the total, respectively. The water vapor at 700 hPa mainly comes from the inland areas of Northwest China and South China Sea. Specifically, the water vapor from inland Northwest China contributes 44% of the total, acting as the primary source. The water vapor at 500 hPa is mainly transported from the inland areas of South China and Northwest China, with that from the inland South China (56%) being more prominent. The water vapor at all levels is mainly transported to the rainstorm region through the eastern and southern regions of China from the source areas. Additionally, there are some differences in the water vapor trajectories at a 6 h interval.

To address the challenges in evaluation of aircraft cloud seeding effect, this study proposes a physical evaluation method that integrates multiple key techniques to achieve an integrated quantitative evaluation based on multiple indicators. This evaluation approach was applied to six aircraft cloud seeding operations conducted in Henan Province of China during 2023–2024. Results show that the six operations exhibited nonlinear growth patterns for dispersion in both the target and control areas, with rapid expansion at the initial stage followed by a slower rate. The dispersion rate, distance, extent and concentration of the seeding agent vary significantly depending on the meteorological conditions, such as atmospheric turbulence, wind speed, stability and humidity. For the six operations, the vertically integrated liquid water content (VIL) showed notably high entropy weights (0.06–0.43) in multiple operations (No. 2, 3 and 6), making it a relatively stable indicator for evaluating the cloud seeding effects. Due to complex cloud microphysical processes such as latent heat release from deposition, downdrafts, cloud dissipation and cloud development, the responses of cloud-top temperature to seeding varied considerably (−44.56 °C ~ −6 °C). The effects of cloud seeding on cloud effective radius and optical thickness are complex and vary substantially depending on specific seeding conditions. The responses of liquid water path were time-dependent, the seeding-induced responses of radar reflectivity exhibited distinct patterns, including delayed manifestation, strong enhancement, and ineffectiveness or being masked. The strong-echo area and the VIL in the target areas fluctuated and generally decreased over time, respectively. The integrated physical inspection dimensionless index (PIDI) values for the seven indicators ranged from 15.0 % to 69.7 %, showing a smaller variation magnitude compared with the change rates of individual indicators, which reflects the synergistic effects of multiple indicators. This study provides a quantifiable and robust framework that mitigates the interference of natural variability, thereby advancing cloud seeding techniques and improving effect evaluation capabilities for artificial precipitation enhancement.

To address the challenges in evaluation of aircraft cloud seeding effect, this study proposes a physical evaluation method that integrates multiple key techniques to achieve an integrated quantitative evaluation based on multiple indicators. This evaluation approach was applied to six aircraft cloud seeding operations conducted in Henan Province of China during 2023–2024. Results show that the six operations exhibited nonlinear growth patterns for dispersion in both the target and control areas, with rapid expansion at the initial stage followed by a slower rate. The dispersion rate, distance, extent and concentration of the seeding agent vary significantly depending on the meteorological conditions, such as atmospheric turbulence, wind speed, stability and humidity. For the six operations, the vertically integrated liquid water content (VIL) showed notably high entropy weights (0.06–0.43) in multiple operations (No. 2, 3 and 6), making it a relatively stable indicator for evaluating the cloud seeding effects. Due to complex cloud microphysical processes such as latent heat release from deposition, downdrafts, cloud dissipation and cloud development, the responses of cloud-top temperature to seeding varied considerably (−44.56 °C ~ −6 °C). The effects of cloud seeding on cloud effective radius and optical thickness are complex and vary substantially depending on specific seeding conditions. The responses of liquid water path were time-dependent, the seeding-induced responses of radar reflectivity exhibited distinct patterns, including delayed manifestation, strong enhancement, and ineffectiveness or being masked. The strong-echo area and the VIL in the target areas fluctuated and generally decreased over time, respectively. The integrated physical inspection dimensionless index (PIDI) values for the seven indicators ranged from 15.0 % to 69.7 %, showing a smaller variation magnitude compared with the change rates of individual indicators, which reflects the synergistic effects of multiple indicators. This study provides a quantifiable and robust framework that mitigates the interference of natural variability, thereby advancing cloud seeding techniques and improving effect evaluation capabilities for artificial precipitation enhancement.