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.

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 pollution by polycyclic aromatic hydrocarbons (PAHs) has become a serious problem, especially in Asia, as PAHs can severely affect ecologically important mountainous areas. Using pine needles and mosses as bio-indicators, this study examined PAH pollution in a mountainous study area and evaluated the influence of transboundary PAHs. PAHs in urban areas were also evaluated for comparison. The study sites were alpine areas and urban areas (inland or coastal cities) across central Japan, in the easternmost part of Asia where atmospheric pollutants are transported from mainland Asia. The mean PAH concentrations of pine needles and mosses were 198.9 ± 184.2 ng g⁻¹ dry weight (dw) and 131.8 ± 60.7 ng g⁻¹ dw (mean ± SD), respectively. Pine needles preferentially accumulated PAHs with low molecular weights (LMW PAHs) and exhibited large differences in both PAH concentration and isomer ratios between alpine and urban sites. These differences can be explained by the strong influence of LMW PAHs emitted from domestic sources, which decreased and changed during transport from urban to alpine sites due to dry/wet deposition and photodegradation. In contrast, mosses accumulated a higher ratio of PAHs with high molecular weight (HMW PAHs). A comparison of isomer ratios showed that the PAH source for alpine moss was similar to that for northern coastal cities, which are typically influenced by long-transported PAHs from East Asia. Thus, these results indicate that alpine moss can also be strongly affected by the transboundary PAHs. It is likely that the uptake characteristics of moss, alpine climate, and alpine locations far from urban areas can strengthen the influence of transboundary pollution. Based on these results, the limitations and most effective use of bioindicators of PAH pollution for preserving alpine ecosystems are discussed.

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.

East Asia is one of the world's largest sources of dust and anthropogenic pollution. Dust particles originating from East Asia have been recognized to travel across the Pacific to North America and beyond, thereby affecting the radiation incident on the surface as well as clouds aloft in the atmosphere. In this study, integrated analyses are performed focusing on one trans-Pacific dust episode during 12–22 March 2015, based on space-borne, ground-based observations, reanalysis data combined with Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), and the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). From the perspective of synoptic patterns, the location and strength of Aleutian low pressure system largely determined the eastward transport of dust plumes towards western North America. Multi-sensor satellite observations reveal that dust aerosols in this episode originated from the Taklimakan and Gobi Deserts. Moreover, the satellite observations suggest that the dust particles can be transformed to polluted particles over the East Asian regions after encountering high concentration of anthropogenic pollutants. In terms of the vertical distribution of polluted dust particles, at the very beginning, they were mainly located in the altitudes ranging from 1 km to 7 km over the source region, then ascended to 2 km–9 km over the Pacific Ocean. The simulations confirm that these elevated dust particles in the lower free troposphere were largely transported along the prevailing westerly jet stream. Overall, observations and modeling demonstrate how a typical springtime dust episode develops and how the dust particles travel over the North Pacific Ocean all the way to North America.

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.

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only.

Global meta-analyses showed that biochar application can reduce N₂O emission. However, no relevant review study is available for East Asian countries which are responsible for 70% of gaseous N losses from croplands globally. This review analyzed data of the biochar-induced N₂O mitigation affected by experimental conditions, including experimental types, biochar types and application rates, soil properties, and chemical forms and application rates of N fertilizer for East Asian countries. The magnitude of biochar-induced N₂O mitigation was evaluated by calculating N₂O reduction index (Rᵢₙdₑₓ, percentage reduction of N₂O by biochar relative to control). The Rᵢₙdₑₓ was further standardized against biochar application rate by calculating Rᵢₙdₑₓ per unit of biochar application rate (ton ha⁻¹) (Unit Rᵢₙdₑₓ). The Rᵢₙdₑₓ averaged across different experimental types (n = 196) was −21.1 ± 2.4%. Incubation and pot experiments showed greater Rᵢₙdₑₓ than column and field experiments due to higher biochar application rate and shorter experiment duration. Feedstock type and pyrolysis temperature also affected Rᵢₙdₑₓ; either bamboo feedstock or pyrolysis at > 400 °C resulted in a greater Rᵢₙdₑₓ. The magnitude of Rᵢₙdₑₓ also increased with increasing biochar rate. Soil properties did not affect Rᵢₙdₑₓ when evaluated across all experimental types, but there was an indication that biochar decreased N₂O emission more at a lower soil moisture level in field experiments. The magnitude of Rᵢₙdₑₓ increased with increasing N fertilizer rate up to 500–600 kg N ha⁻¹, but it decreased thereafter. The Unit Rᵢₙdₑₓ averaged across experimental types was −1.2 ± 0.9%, and it was rarely affected by experimental type and conditions but diminished with increasing biochar rate. Our results highlight that since N₂O mitigation by biochar is affected by biochar application rate, Rᵢₙdₑₓ needs to be carefully evaluated by standardizing against biochar application rate to suggest the best conditions for biochar usage in East Asia.

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.

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.