Forest health condition was evaluated on 111 terrestrial permanent monitoring plots. Image classification for the whole region was done by using regression equation between data from the terrestrial survey and digital value of original and derived synthetic bands of Landsat TM. It was found that synthetic channels give better result than original bands. The change analysis was carried out by the method of image diffferences in image pairs. Output images were standardized and than reclassified into 6 classes. It was found that difference of vegetation indexes between two years gives better result than simple difference between two independent classified images of forest condition

Black carbon (BC) exposure in China continues to be relatively high, prompting researchers to assess BC exposure levels using data from monitoring sites, satellite remote sensing, and models. However, data regarding the application of a combined strategy comprising the analysis of monitoring data and various types of data to simulate BC exposure levels are lacking. Hence, the current study seeks to estimate short- and long-term BC exposure levels by combining national monitoring data with data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Furthermore, this study attempts to improve the spatio-temporal resolution of BC exposure levels using Bayesian maximum entropy (BME). The BME model performed well in terms of estimating short- (R² = 0.74 and RMSE = 1.76 μg/m³) and long-term (R² = 0.76 and RMSE = 1.3 μg/m³) exposure. Premature mortalities and economic losses were also assessed by applying localised concentration–response coefficients simulated in China. A total of 74,500 (95% confidence interval (CI): 23,900–124,500) and 538,400 (95% CI: 495,000–581,300) all-cause premature mortality cases were found to be associated with short- and long-term BC exposure, respectively. Meanwhile, short-term BC exposure was associated with economic losses ranging from 7.5 to 13.2 billion US dollars (USD) (1 USD = 6.36 RMB on January 19, 2022) based on amended human capital (AHC) and willingness to pay (WTP), accounting for 0.06%–0.1% of China's total gross domestic product (GDP) in 2017 (1.2 × 10⁴ billion USD), respectively. The economic losses for long-term exposure varied from 53 to 93.2 billion USD based on AHC and WTP, accounting for 0.4%–0.8% of China's total GDP in 2017, respectively.

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.

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.

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.

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO₂). Current studies in China at the national scale were less focused on NO₂ exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO₂ predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO₂, TROPOspheric Monitoring Instrument NO₂, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO₂ concentrations from 2013 to 2019 across China at 1×1 km² resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R² = 0.72) and the spatial (R² = 0.85) variations of the NO₂ predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R² > 0.68) or regions far away from monitors (CV R² > 0.63). We identified a clear decreasing trend of NO₂ exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%–14% in some megacities and captured substantial NO₂ variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

Atmospheric nitrogen dioxide (NO₂) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO₂ monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO₂ from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO₂ with high accuracy, with a coefficient of determination (R²) of 0.89 and a root mean squared error of 5.8 μg/m³ for sample-based 10-fold cross-validation. Based on the surface NO₂ concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO₂ pollution in north China. We found substantial drops in surface NO₂ concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO₂ exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO₂ among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO₂ concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO₂ dataset, can benefit air quality monitoring, forecasting, and health burden assessments.

Long-range transport (LRT) reportedly carries air pollutants and microorganisms to downwind areas. LRT can be of various types, such as dust storm (DS) and frontal pollution (FP); however, studies comparing their effects on bioaerosols are lacking. This study evaluated the effect of LRT on viral and bacterial concentrations in Northern Taiwan. When LRT occurred and possibly affected Taiwan from August 2013 to April 2014, air samples (before, during, and after LRT) were collected in Cape Fugui (CF, Taiwan’s northernmost point) and National Taiwan University (NTU). Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was applied to quantify influenza A virus. qPCR and qPCR coupled with propidium monoazide were, respectively, used to quantify total and viable bacteria. Types and occurrence of LRT were confirmed according to the changing patterns of meteorological factors and air pollution, air mass sources (HYSPLIT model), and satellite images. Two Asian DS and three FP cases were included in this study. Influenza A virus was detected only on days before and during FP occurred on January 3–5, 2014, with concentrations of 0.87 and 10.19 copies/m³, respectively. For bacteria, the increase in concentrations of total and viable cells during Asian DSs (17–19 and 25–29 November 2013) was found at CF only (from 3.13 to 3.40 and from 2.62 to 2.85 log copies/m³, respectively). However, bacterial levels at NTU and CF both increased during FP and lasted for 2 days after FP. In conclusion, LRT increased the levels of influenza A virus and bacteria in the ambient air of Northern Taiwan, particularly at CF. During and 2 days (at least) after LRT, people should avoid outdoor activities, especially in case of FP.

The space-borne measured fine-mode aerosol optical depth (fAOD) is a gross index of column-integrated anthropogenic particulate pollutants, especially over the populated land. The fAOD is the product of the AOD and the fine-mode fraction (FMF). While there exist numerous global AOD products derived from many different satellite sensors, there have been much fewer, if any, global FMF products with a quality good enough to understand their spatiotemporal variations. This is key to understanding the global distribution and spatiotemporal variations of air pollutants, as well as their impacts on global environmental and climate changes. Modifying our newly developed retrieval algorithm to the latest global-scale Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product (Collection 6.1), a global 10-year FMF product is generated and analyzed here. We first validate the product through comparisons with the FMF derived from Aerosol Robotic Network (AERONET) measurements. Among our 169,313 samples, the satellite-derived FMFs agreed with the AERONET spectral deconvolution algorithm (SDA)-retrieved FMFs with a root-mean-square error (RMSE) of 0.22. Analyzed using this new product are the global patterns and interannual and seasonal variations of the FMF over land. In general, the FMF is large (>0.80) over Mexico, Myanmar, Laos, southern China, and Africa and less than 0.5 in the Sahelian and Sudanian zones of northern Africa. Seasonally, higher FMF values occur in summer and autumn. The linear trend in the satellite-derived and AERONET FMFs for different countries was explored. The upward trend in the FMFs was particularly strong over Australia since 2008. This study provides a new global view of changes in FMFs using a new satellite product that could help improve our understanding of air pollution around the world.

Perennial contaminated groundwater seepage is threatening the downstream ecosystem of the Kazipally Pharmaceutical industrial area located in South India. The sources of seepage are unknown for the last three decades that challenging the regulatory authorities and industries. In general, water quality monitoring and geophysical techniques are applied to identify the sources. However, these techniques may lead to ambiguous results and fail to identify the seepage sources, especially when the area is urbanized/paved, and groundwater is already contaminated with other leakage sources that have similar chemical compounds. In the present study, a novel and multidisciplinary approach were adopted that includes satellite-based Land Surface Temperature (LST) observations, field-based Electrical Resistivity Tomography (ERT), continuous Soil Electrical Conductivity (SEC) and Volumetric Soil Moisture (VSM%) measurements along with groundwater levels monitoring to identify the sources and to control the seepage. The integrated results identified that the locations with the Standard Thermal Anomaly (STA) in the range of −0.5 to -1 °C, VSM% >50%, SEC > 1.5 mS/cm, bulk resistivity < 12 Ω m with shallow groundwater levels < 3 m below ground level (bgl) are potentially contaminated perennial seepage sources. Impermeable sheet piles have been installed across the groundwater flow direction to control the seepage up to 1.5 m bgl, where groundwater frequently intercepts land surface. The quantity of dry season groundwater seepage has been declined by 79.2% after these interventions, which in turn minimized the treatment cost of 1,96,283 USD/year and improved the downstream ecosystem.