Satellite-Based Estimates of Wet Ammonium (NH4-N) Deposition Fluxes Across China during 2011-2016 Using a Space-Time Ensemble Model

Atmospheric wet organic nitrogen deposition in China: Insights from the national observation network

Organic nitrogen (N) is an important component of atmospheric reactive N deposition, and its bioavailability is almost as important as that of inorganic N. Currently, there are limited reports of national observations of organic N deposition; most stations are concentrated in rural and urban areas, with even fewer long-term observations of natural ecosystems in remote areas. Based on the China Wet Deposition Observation Network, this study regularly collected monthly wet deposition samples from 43 typical ecosystems from 2013 to 2021 and measured related N concentrations. The aim was to provide a more comprehensive assessment of the multi-component characteristics of atmospheric wet N deposition and reveal the influencing factors and potential sources of wet dissolved organic N (DON) deposition. The results showed that atmospheric wet deposition fluxes of NO3-, NH4+, DON and dissolved total N (DTN) were 4.68, 5.25, 4.32, and 13.05 kg N ha-1 yr-1, respectively, and that DON accounted for 30 % of DTN deposition (potentially up to 50 % in remote areas). Wet DON deposition was related to anthropogenic emissions (agriculture, biomass burning, and traffic), natural emissions (volatile organic compound emissions from vegetation), and precipitation processes. The wet DON deposition flux was higher in South, Central, and Southwest China, with more precipitation and intensive agricultural activities or more vegetation cover, and lower in Northwest China and Inner Mongolia, with less precipitation and human activities or vegetation cover. DON was the main contributor to DTN deposition in remote areas and was possibly related to natural emissions. In rural and urban areas, DON may have been more influenced by agricultural activities and anthropogenic emissions. This study quantified the long-term spatiotemporal patterns of wet N deposition and provides a reference for future N addition experiments and N cycle studies. Further consideration of DON deposition is required, especially in the context of anthropogenic control of NO2 and NH3.

A database of atmospheric inorganic nitrogen deposition fluxes in China from satellite monitoring

Over the past century, atmospheric inorganic nitrogen (IN) deposition to terrestrial ecosystems has significantly increased and caused various environmental issues. China has been one of the hotspot regions for IN deposition, yet limited data exist regarding IN deposition fluxes in China at the regional scale. In this study, based on NO2 and NH3 columns acquired by satellite sensors, coupled with atmospheric chemical transport model (CTM), mixed-effects model and site observations, we constructed regional-scale IN dry and wet deposition models respectively, and finally proposed a spatially explicit database of IN deposition fluxes in China. The database includes the dry, wet and total deposition fluxes in China during 2011-2020, and the data are presented in raster form with a resolution of 0.25° × 0.25°. Overall, the database is of great importance for monitoring and simulating the trends of IN deposition over a long time series in China.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Atmospheric Reactive Nitrogen Deposition from 2010 to 2021 in Lake Taihu and the Effects on Phytoplankton

The effects of nitrogen deposition reduction on nutrient loading in freshwaters have been widely studied, especially in remote regions. However, understanding of the ecological effects is still rather limited. Herein, we re-estimated nitrogen deposition, both of wet and dry deposition, in Lake Taihu with monthly monitoring data from 2010 to 2021. Our results showed that the atmospheric deposition of reactive nitrogen (namely NH₄⁺ and NO₃^-) in Lake Taihu was 4.94-11.49 kton/yr, which equaled 13.9%-27.3% of the riverine loading. Dry deposition of NH₄⁺ and NO₃^- contributed 53.1% of the bulk deposition in Lake Taihu. Ammonium was the main component of both wet and dry deposition, which may have been due to the strong agriculture-related activities around Lake Taihu. Nitrogen deposition explained 24.9% of the variation in phytoplankton community succession from 2010 to 2021 and was the highest among all the environmental factors. Atmospheric deposition offset the effects of external nitrogen reduction during the early years and delayed the emergence of nitrogen-fixing cyanobacterial dominance in Lake Taihu. Our results implied that a decrease in nitrogen deposition due to a reduction in fertilizer use, especially a decrease in NH₄⁺ deposition, could limit diatoms and promote non-nitrogen-fixing cyanobacterial dominance, followed by nitrogen-fixing taxa. This result was also applied to other shallow eutrophic lakes around the middle and lower reaches of the Yangtze River, where significant reduction of fertilizer use recorded during the last decades.

Quantifying the wet deposition of reactive nitrogen over China: Synthesis of observations and models

Accurate estimation on reaction nitrogen (N_r) deposition is highly demanded for assessing the impacts on the environment and human beings. This study investigated the wet deposition of inorganic nitrogen (IN) in mainland China by measurements from over 500 sites from five observational networks/databases and ensemble results of eleven chemical transport models (CTMs). Each data source has its focus and limitations and together formed a comprehensive view over China. But the inconsistency among different sources may hinder the appropriate usage of data. Model evaluation results demonstrated the models' deficiency in simulating the wet NO₃^- deposition over Southeast China (40 % underestimation) and showed an overall underestimation of wet NH₄⁺ deposition over the hotspot regions (5-60 % underestimation). A synthesis of this study and twelve reference studies was conducted to quantify the national amount of wet IN deposition. The estimations by CTMs ranged 2.4-3.9 Tg(N) yr^-1 for wet NO_y deposition and 4-6.7 Tg(N) yr^-1 for wet NH_x deposition, after adjusting the results with 10-19 % underestimations in wet NO_y deposition and 1-40 % underestimations in wet NH_x deposition. The estimations by ground observations ranged 7.1-9 Tg(N) yr^-1 for wet NO_y deposition and 8-13.1 Tg(N) yr^-1 for wet NH_x deposition, which were 20-275 % higher than the estimation by CTMs, but the results were strongly influenced by the abundances and representative of measurements. Studies using statistical techniques to interpolate site observations predicted 3-5.5 Tg(N) yr^-1 for wet NO_y deposition and 3.9-7.2 Tg(N) yr^-1 for wet NH_x deposition. This approach benefited from high accuracy and good robustness of the statistical models, but the uncertainty in the interpolation methods could be a potential drawback.

Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method

A non-parametric ensemble model was proposed to estimate the long-term (2015-2019) particle surface area concentrations (S_A) over China for the first time on basis of a vilification dataset of measured particle number size distribution. This ensemble model showed excellent cross-validation R² value (CV R² = 0.83) as well as a relatively low root-mean-square error (RMSE = 195.0 μm²/cm³). No matter in which year, considerable spatial heterogeneity of S_A was found over China with higher S_A in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Middle Lower Reaches of Yangtze River (MLYR). From 2015 to 2019, S_A significantly decreased in representative city clusters. The reduction rates were 140.1 μm²·cm^-3·a^-1 in BTH, 110.7 μm²·cm^-3·a^-1 in Pearl River Delta (PRD), 105.2 μm²·cm^-3·a^-1 in YRD, and 92.4 μm²·cm^-3·a^-1 in Sichuan Basin (SCB), respectively. Even though such quick reduction, high S_A (ranged from ~800 μm²/cm³ to ~1750 μm²/cm³) during the heavy pollution period (PM_2.5 > 75 μg/m³) still existed in the above-mentioned city clusters and may provide rich reaction vessels for multiphase chemistry. A dichotomy of enhanced annual 4th maximum daily 8-h average O₃ concentrations (4MDA8 O₃) and decreased S_A during summertime was found in Shanghai, a representative city of YRD. In Chengdu (SCB), increased 4MDA8 O₃ concentration was associated with a synchronous increase of S_A from 2017 to 2019. Differently, 4MDA8 O₃ concentrations enhanced in Beijing (BTH) and Guangzhou (PRD), while not significant for S_A before 2018. This work will greatly deepen our understanding of the historical variation and spatial distributions of S_A over China.