Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM₂.₅ concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM₂.₅ concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from −56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM₂.₅ concentrations over inland areas ranging from 0.27 ppbV to −7.17 ppbV and 0.89 μg/m³ to −2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to −2.86 ppbV and 3.41 μg/m³ to −7.47 μg/m³. The decreases in inland ozone and PM₂.₅ concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM₂.₅ formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM₂.₅ concentrations due to reduced reactions of PM₂.₅ precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM₂.₅ concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM₂.₅ inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Light-absorbing organic aerosols, also known as brown carbon (BrC), enhance the warming effect of the Earth’s atmosphere. The seasonal and spatial variability of BrC absorption properties is poorly constrained and accounted for in the climate models resulting in a substantial underestimation of their radiative forcing estimates. This study reports seasonal and spatial variability of absorption properties and simple forcing efficiency of light-absorbing water-soluble organic carbon (WSOC, SFEWSOC) by utilizing current and previous field-based measurements reported mostly from Asia along with a few observations from Europe, the USA, and the Amazon rainforest. The absorption coefficient of WSOC at 365 nm (bₐbₛ₋₃₆₅) and the concentrations of carbonaceous species at Kanpur were about an order of magnitude higher during winter than in the monsoon season owing to differences in the boundary layer height, active sources and their strengths, and amount of seasonal wet precipitation. The WSOC aerosols during winter exhibited ∼1.6 times higher light absorption capacity than in the monsoon season at Kanpur site. The assessment of spatial variability of the imaginary component of the refractive index spectrum (kλ) across South Asia has revealed that it varies from ∼1 to 2 orders of magnitude and light absorption capacity of WSOC ranges from 3 to 21 W/g. The light absorption capacity of WSOC aerosols exhibited less spatial variability across East Asia (5–13 W/g) when compared to that in the South Asia. The photochemical aging of WSOC aerosols, indicated by the enhancement in WSOC/OC ratio, was linked to degradation in their light absorption capacity, whereas the absorption Ångström exponent (AAE) remained unaffected. This study recommends the adoption of refined climate models where sampling regime specific absorption properties are calculated separately, such that these inputs can better constrain the model estimates of the global effects of BrC.

A total of 82 aerosol samples (PM10-2.5) were collected from June 18, 2015 to October 1, 2016 at the remote sea site, the Ieodo Ocean Research Station (IORS), in the East China Sea. Samples were analyzed for 10 elements (Al, Fe, Cu, Zn, As, Mo, Cd, Sb, Tl, and Pb) as well as Pb isotopic composition to characterize temporal variations in elemental concentration levels, and to identify the potential source regions of atmospheric pollutants transported over the remote East China Sea. The results showed that the annual average element concentrations were lowest compared to those at different sites in East Asia, suggesting a very clean background area of IORS, with values ranging from 114 ng m⁻³ for Al to 0.045 ng m⁻³ for Tl. Concentrations averaged seasonally for all the elements revealed the highest levels occurring between winter and spring, and the lowest levels in summer. High enrichment factors (EF) of more than 100 for trace elements suggest that these elements originated mostly from anthropogenic sources. Coupling the Pb isotopic composition with a back trajectory analysis identified the potential source regions for each sample. Our approach identified China as a dominant contributor affecting atmospheric composition changes at IORS, the remote area of the East China Sea. As the largest anthropogenic emission source in East Asia, China contributed to almost 100% of the elemental concentration levels in winter and spring, ∼53% in summer and ∼63% in autumn. Because IORS's ambient air is sensitive to even slight changes in pollutant loading due to the significantly low pollution levels, long-term monitoring of air quality at IORS will provide invaluable information on the progress and efforts of atmospheric pollution management linked to emission controls in East Asian countries, especially China.

During the past two centuries metal loads in the Earth's atmosphere and ecosystems have increased significantly over pre-industrial levels. This has been associated with deleterious effects to ecosystem processes and human health. The magnitude of this toxic metal burden, as well as the spatial and temporal patterns of metal enrichment, is recorded in sedimentary archives across the globe. This paper presents a compilation of selected Pb contamination records from lakes (n = 10), peat mires (n = 10) and ice fields (n = 7) from Europe, North and South America, Asia, Australia and the Northern and Southern Hemisphere polar regions. These records quantify changes in Pb enrichment in remote from source environments. The presence of anthropogenic Pb in the environment has a long history, extending as far back as the early to mid-Holocene in North America, Europe and East Asia. However, results show that Pb contamination in the Earth's environment became globally ubiquitous at the beginning of the Second Industrial Revolution (c.1850–1890 CE), after which the magnitude of Pb contamination increased significantly. This date therefore serves as an effective global marker for the onset of the Anthropocene. Current global average Pb enrichment rates are between 6 and 35 times background, however Pb contamination loads are spatially variable. For example, they are >100 times background in Europe and North America and 5–15 times background in Antarctica. Despite a recent decline in Pb loads in some regions, most notably Europe and North America, anthropogenic Pb remains highly enriched and universally present in global ecosystems, while concentrations are increasing in some regions (Australia, Asia and parts of South America and Antarctica). There is, however, a paucity of Pb enrichment records outside of Europe, which limits assessments of global contamination.

To assess the effects of elevated concentrations of carbon dioxide (CO2) and ozone (O3) on the growth of two mid-successional oak species native to East Asia, Quercus mongolica var. crispula and Quercus serrata, we measured gas exchange and biomass allocation in seedlings (initially 1-year-old) grown under combinations of elevated CO2 (550 μmol mol−1) and O3 (twice-ambient) for two growing seasons in an open-field experiment in which root growth was not limited. Both the oak species showed a significant growth enhancement under the combination of elevated CO2 and O3 (indicated by total dry mass; over twice of ambient-grown plants, p < .05), which probably resulted from a preferable biomass partitioning into leaves induced by O3 and a predominant enhancement of photosynthesis under elevated CO2. Such an over-compensative response in the two Japanese oak species resulted in greater plant growth under the combination of elevated CO2 and O3 than elevated CO2 alone.

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM₁₀) and <2.5 μm (PM₂.₅) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM₁₀ and PM₂.₅ were R² = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM₁₀ and PM₂.₅ were R² = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO₂ and O₃ from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia. The machine learning was adopted by fusion of various satellite-based variables, numerical model-based meteorological variables, and land-use variables. Four machine learning approaches—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB), and Light Gradient Boosting Machine (LGBM)—were evaluated and compared with Multiple Linear Regression (MLR) as a base statistical method. This study also modeled the NO₂ and O₃ concentrations over the ocean surface (i.e., land model for scheme 1 and ocean model for scheme 2). The estimated surface concentrations were validated through three cross-validation approaches (i.e., random, temporal, and spatial). The results showed that the NO₂ model produced R² of 0.63–0.70 and normalized root-mean-square-error (nRMSE) of 38.3–42.2% and the O₃ model resulted in R² of 0.65–0.78 and nRMSE of 19.6–24.7% for scheme 1. The indirect validation based on the stations near the coastline for scheme 2 showed slight decrease (~0.3–2.4%) in nRMSE when compared to scheme 1. The contributions of input variables to the models were analyzed based on SHapely Additive exPlanations (SHAP) values. The NO₂ vertical column density among the TROPOMI-derived variables showed the largest contribution in both the NO₂ and O₃ models.

East Asian oceans are possibly affected by a high nitrogen (N) burden because of the intense anthropogenic emissions in this region. Based on high-resolution regional chemical transport modeling with horizontal grid scales of 36 and 12 km, we investigated the N burden into East Asian oceans via atmospheric deposition in 2010. We found a high N burden of 2–9 kg N ha⁻¹ yr⁻¹ over the Yellow Sea, East China Sea (ECS), and Sea of Japan. Emissions over East Asia were dominated by ammonia (NH₃) over land and nitrogen oxides (NOₓ) over oceans, and N deposition was dominated by reduced N over most land and open ocean, whereas it was dominated by oxidized N over marginal seas and desert areas. The verified numerical modeling identified that the following processes were quantitatively important over East Asian oceans: the dry deposition of nitric acid (HNO₃), NH₃, and coarse-mode (aerodynamic diameter greater than 2.5 μm) NO₃⁻, and wet deposition of fine-mode (aerodynamic diameter less than 2.5 μm) NO₃⁻ and NH₄⁺. The relative importance of the dry deposition of coarse-mode NO₃⁻ was higher over open ocean. The estimated N deposition to the whole ECS was 390 Gg N yr⁻¹; this is comparable to the discharge from the Yangtze River to the ECS, indicating the significant contribution of atmospheric deposition. Based on the high-resolution modeling over the ECS, a tendency of high deposition in the western ECS and low deposition in the eastern ECS was found, and a variety of deposition processes were estimated. The dry deposition of coarse-mode NO₃⁻ and wet deposition of fine-mode NH₄⁺ were the main factors, and the wet deposition of fine-mode NO₃⁻ over the northeastern ECS and wet deposition of coarse-mode NO₃⁻ over the southeastern ECS were also found to be significant processes determining N deposition over the ECS.

This work aimed at predicting the toxic effects of phenolic compounds in Ba River on the health of female sharpbelly (Hemiculter lucidus) by the de novo transcriptomic analysis of the liver. Sharpbelly, a native fish living in freshwater ecosystem of East Asia, were sampled upstream, near, and downstream of a wastewater discharge to the Ba river. Based on the occurrence of bisphenol A (BPA), nonylphenol (NP), and 4-tert-octylphenol (4-t-OP) in the water and fish sampled from each site, up-, mid-, and down-stream were interpreted as control, high, and low treatment groups, respectively. In the mid-stream group the Fulton’s condition factor (CF) and body weight were remarkably increased by approximate 20%; the gonado-somatic index (GSI) and hepatosomatic index (HSI) in mid-stream fish showed a similar increasing trend but lacking of statistical difference. Exposure to wastewater effluent caused 160 and 162 differentially expressed genes (DEGs) in up-mid and down-mid stream groups, respectively. Two sets of DEGs were primarily enriched in the signaling pathways of drug metabolism, endocrine system, cellular process, and lipid metabolism in the mid-stream sharpbelly, which may alter the fish behavior, disrupt the reproductive function, and lead to hypothyroidism, hepatic steatosis, etc. Taken together, our results linked the disrupted signaling pathways with activities of phenolic compounds to predict the potential effects of wastewater effluent on the health of wild fish.

Atmospheric deposition, either dry or wet, has been identified as an important pathway of mercury (Hg) input to terrestrial and aquatic systems. Although East Asia is the major atmospheric Hg emission source region, very few studies have been conducted to quantify atmospheric Hg deposition in its downwind region. In this study, 8-year (2009–2016) atmospheric Hg dry deposition was reported at the Lulin Atmospheric Background Station (LABS), a high mountain forest site in central Taiwan. Dry deposition of speciated Hg was estimated using a bi-directional air-surface flux exchange model for gaseous elemental mercury (GEM) and dry deposition models for gaseous oxidized mercury (GOM) and particulate-bound mercury (PBM), making use of the monitored speciated atmospheric Hg concentrations. Annual total Hg dry deposition ranged from 51.9 to 84.9 μg m−2 yr−1 with a multi-year average of 66.1 μg m−2 yr−1. Among the three forms of atmospheric Hg, GEM was the main contributor to the total dry deposition, contributing about 77.8% to the total, due to the high density of forest canopy as well as the much higher concentration of GEM than GOM and PBM at LABS. Mercury dry deposition is higher in winter and spring than in summer and fall, partly due to the elevated Hg concentrations associated with air masses from East and Southeast Asia where with high atmospheric Hg emissions. The mean annual dry/wet deposition ratio of 2.8 at LABS indicated that Hg deposition to forest landscape was governed by dry rather than wet deposition.