PM₂.₅ (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM₂.₅ distribution is very helpful for PM₂.₅ pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM₂.₅ estimation model in China. Hourly cross-validation results indicated that estimated PM₂.₅ values were consistent with the site observation values, with an R² range of 0.82–0.91 and root mean square error (RMSE) of 8.79–14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R² > 0.9). Analysis of the correlation coefficient between important features and PM₂.₅ showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00–11:00, and then began to decline. High-resolution PM₂.₅ concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi ‘an, Wuhan, and Chengdu. Our model can also capture the direction of PM₂.₅, which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM₂.₅ pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM₂.₅ concentrations typically occur near cities.

Previous studies have indicated that outdoor light at night (LAN) is associated with a higher prevalence of overweight or obesity in adults. However, the association of LAN levels with overweight or obesity in children is still unknown. This study utilized data from the Seven Northeastern Cities study, which included 47,990 school-aged children and adolescents (ages 6–18 years). Outdoor LAN levels were measured using satellite imaging data. Weight and height were used to calculate age-sex-specific body mass index (BMI) Z-scores based on the World Health Organization (WHO) growth standards. Overweight status and obesity were defined using the Chinese standard. Information regarding socioeconomic status, sleep-related characteristics, and obesogenic factors were obtained using a questionnaire. A generalized linear mixed model examined the associations of outdoor LAN levels (in quartiles) with the outcomes of interest. Compared to children in the lowest quartile of outdoor LAN levels, children exposed to higher outdoor LAN levels had larger BMI Z-scores and higher odds of being overweight (including obesity) or obese, with the largest estimates in the third quartile [BMI Z-score: β = 0.26, 95% CI: 0.18–0.33; overweight (including obesity): OR = 1.40, 95% CI: 1.25–1.56; obesity: OR = 1.46, 95% CI: 1.29–1.65]. There was a significant sex difference (Pᵢₙₜₑᵣₐcₜᵢₒₙ<0.001) in the association of outdoor LAN levels with BMI Z-scores, and the association was stronger in males. Results remained robust following multiple sensitivity analyses and the adjustment of sleep-related characteristics, obesogenic factors, and environmental exposures. Our findings suggest that higher outdoor LAN levels are associated with larger BMI Z-scores and greater odds of overweight (including obesity) and obesity in school-aged children and adolescents. Further, the association between outdoor LAN levels and BMI Z-scores is stronger in males. Future studies with exposure assessments that consider both outdoor and indoor LAN exposures are needed.

Several studies have reported an association between residential surrounding particulate matter with an aerodynamic diameter ≤2.5 μm (PM₂.₅) and coronary heart disease (CHD). However, the underlying biological mechanism remains unclear. To fill this research gap, this study enrolled a residentially stable sample of 942 patients with CHD and 1723 controls. PM₂.₅ concentration was obtained from satellite-based annual global PM₂.₅ estimates for the period 1998–2019. MicroRNA microarray and pathway analysis of target genes was performed to elucidate the potential biological mechanism by which PM₂.₅ increases CHD risk. The results showed that individuals exposed to high PM₂.₅ concentrations had higher risks of CHD than those exposed to low PM₂.₅ concentrations (odds ratio = 1.22, 95% confidence interval: 1.00, 1.47 per 10 μg/m³ increase in PM₂.₅). Systolic blood pressure mediated 6.6% of the association between PM₂.₅ and CHD. PM₂.₅ and miR-4726-5p had an interaction effect on CHD development. Bioinformatic analysis demonstrated that miR-4726-5p may affect the occurrence of CHD by regulating the function of RhoA. Therefore, individuals in areas with high PM₂.₅ exposure and relative miR-4726-5p expression have a higher risk of CHD than their counterparts because of the interaction effect of PM₂.₅ and miR-4726-5p on blood pressure.

Groundwater nitrate (NO₃⁻) pollution is a worldwide environmental problem. Therefore, identification and partitioning of its potential sources are of great importance for effective control of groundwater quality. The current study was carried out to identify the potential sources of groundwater NO₃⁻ pollution and determine their apportionment in different land use/land cover (LULC) types in a traditional agricultural area, Weining Plain, in Northwest China. Multiple hydrochemical indices, as well as dual NO₃⁻ isotopes (δ¹⁵N–NO₃ and δ¹⁸O–NO₃), were used to investigate the groundwater quality and its influencing factors. LULC patterns of the study area were first determined by interpreting remote sensing image data collected from the Sentinel-2 satellite, then the Bayesian stable isotope mixing model (MixSIAR) was used to estimate proportional contributions of the potential sources to groundwater NO₃⁻ concentrations. Groundwater quality in the study area was influenced by both natural and anthropogenic factors, with anthropological impact being more important. The results of LULC revealed that the irrigated land is the dominant LULC type in the plain, covering an area of 576.6 km² (57.18% of the total surface study area of the plain). On the other hand, the results of the NO₃⁻ isotopes suggested that manure and sewage (M&S), as well as soil nitrogen (SN), were the major contributors to groundwater NO₃⁻. Moreover, the results obtained from the MixSIAR model showed that the mean proportional contributions of M&S to groundwater NO₃⁻ were 55.5, 43.4, 21.4, and 78.7% in the forest, irrigated, paddy, and urban lands, respectively. While SN showed mean proportional contributions of 29.9, 43.4, 61.5, and 12.7% in the forest, irrigated, paddy, and urban lands, respectively. The current study provides valuable information for local authorities to support sustainable groundwater management in the study region.

South Korea has experienced a rapid increase in ozone concentrations in surface air together with China for decades. Here we use a 3-D global chemical transport model, GEOS-Chem nested over East Asia (110 E - 140 E, 20 N–50 N) at 0.25° × 0.3125° resolution, to examine locally controllable (domestic anthropogenic) versus uncontrollable (background) contributions to ozone air quality at the national scale for 2016. We conducted model simulations for representative months of each season: January, April, July, and October for winter, spring, summer, and fall and performed extensive model evaluation by comparing simulated ozone with observations from satellite and surface networks. The model appears to reproduce observed spatial and temporal ozone variations, showing correlation coefficients (0.40–0.87) against each observation dataset. Seasonal mean ozone concentrations in the model are the highest in spring (39.3 ± 10.3 ppb), followed by summer (38.3 ± 14.4 ppb), fall (31.2 ± 9.8 ppb), and winter (24.5 ± 7.9 ppb), which is consistent with that of surface observations. Background ozone concentrations obtained from a sensitivity model simulation with no domestic anthropogenic emissions show a different seasonal variation in South Korea, showing the highest value in spring (46.9 ± 3.4 ppb) followed by fall (38.2 ± 3.7 ppb), winter (33.0 ± 1.9 ppb), and summer (32.1 ± 6.7 ppb). Except for summer, when the photochemical formation is dominant, the background ozone concentrations are higher than the seasonal ozone concentrations in the model, indicating that the domestic anthropogenic emissions play a role as ozone loss via NOₓ titration throughout the year. Ozone air quality in South Korea is determined mainly by year-round regional background contributions (peak in spring) with summertime domestic ozone formation by increased biogenic VOCs emissions with persistent NOₓ emissions throughout the year. The domestic NOₓ emissions reduce MDA8 ozone around large cities (Seoul and Busan) and hardly increase MDA8 in other regions in spring, but it increases MDA8 across the country in summer. Therefore, NOₓ reduction can be effective in control of MDA8 ozone in summer, but it can have rather countereffect in spring.

Understanding the processes that underlay an ecological disaster represents a major scientific challenge. Here, we investigated phytoplankton and zooplankton community changes before and during a fauna mass kill in a European protected wetland. Evidence on gradual development and collapse of harmful phytoplankton blooms, allowed us to delineate the biotic and abiotic interactions that led to this ecological disaster. Before the mass fauna kill, mixed blooms of known harmful cyanobacteria and the killer alga Prymnesium parvum altered biomass flow and minimized zooplankton resource use efficiency. These blooms collapsed under high nutrient concentrations and inhibitory ammonia levels, with low phytoplankton biomass leading to a dramatic drop in photosynthetic oxygenation and a shift to a heterotrophic ecosystem phase. Along with the phytoplankton collapse, extremely high numbers of red planktonic crustaceans-Daphnia magna, visible through satellite images, indicated low oxygen conditions as well as a decrease or absence of fish predation pressure. Our findings provide clear evidence that the mass episode of fish and birds kill resulted through severe changes in phytoplankton and zooplankton dynamics, and the alternation on key abiotic conditions. Our study highlights that plankton-related ecosystem functions mirror the accumulated heavy anthropogenic impacts on freshwaters and could reflect a failure in conservation and restoration measures.

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.

Fine particulate matter (PM₂.₅) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM₂.₅ measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM₂.₅ estimates in a study area. In this study, we used AOD estimates of surface PM₂.₅ together with meteorological and land use variables to estimate monthly PM₂.₅ concentrations at a spatial resolution of 3 km² over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R² of 0.98 with a root mean square error (RMSE) of 1.40 μg/m3. The leave-one-out cross-validation (LOOCV) R² with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m3, whereas the R² and RMSE obtained by using the pure random forest approach produced R² and RMSE values of 0.74 and 4.60 μg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM₂.₅ estimates over the entire island, which has complex terrain in terms of land use and topography.

Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM₂.₅) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM₂.₅ predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM₂.₅ prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM₂.₅ predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R² of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM₂.₅ predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM₂.₅ prediction models. We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM₂.₅ prediction is necessary for accurate and reliable PM₂.₅ predictions.

Recent studies suggested that long-term exposure to fine particulate matter (PM₂.₅) was related to a higher risk of dementia incidence or hospitalizations in western populations, but the evidence is limited in Asian cities. Here we explored the link between long-term PM₂.₅ exposure and dementia incidence in the Hong Kong population and whether it varied by population sub-group. We utilized a Hong Kong Chinese cohort of 66,820 people aged ≥65 years who were voluntarily enrolled during 1998–2001 and were followed up to 2011. Prevalent dementia cases were excluded based on the face-to-face interview at baseline. We ascertained the first occurrence of hospitalization for all-cause dementia and major subtypes during the follow-up period. We assessed PM₂.₅ concentrations using a satellite data-based model with a 1 × 1 km² resolution on the residential address. Cox proportional hazards models were adopted to estimate associations of annual mean PM₂.₅ exposure with dementia incidence, adjusting for potential confounders. We identified 1183 incident cases of all-cause dementia during the follow-up period, of which 655 (55.4%) were cases of Alzheimer’s disease, and 334 (28.2%) were those of vascular dementia. We found a positive association between annual mean PM₂.₅ exposure and all-cause dementia incidence in the fully adjusted model. The estimated hazard ratio was 1.06 (95% confidence interval (CI): 1.00, 1.13) per every 3.8 μg/m³ increase in annual mean PM₂.₅ exposure. And the estimated HRs for Alzheimer’s disease and vascular dementia were 1.03 (95% CI: 0.94, 1.12) and 1.09 (95% CI: 0.98, 1.22), respectively. We did not find effect modifications by age, sex, BMI, hypertension, diabetes, or heart disease on the associations. Results suggest that long-term exposure to PM₂.₅ is associated with a higher risk of dementia incidence in the Asian population.