Estimating PM2.5 in the Beijing-Tianjin-Hebei Region Using MODIS AOD Products from 2014 to 2015

Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km2, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. To monitor ground-level PM2.5 concentration, the Chinese government spent significant expense in building more than 1500 in-situ stations (79 stations in the BTH region). However, most of these stations are situated in urban areas. Besides, each station can only represent a limited area around that station, which leaves the vast rural land out of monitoring. In this situation, geographic information system and remote sensing can be used as complementary tools. Traditional models have used 10 km MODIS Aerosol Optical Depth (AOD) product and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimation with a higher resolution became possible. This study presents an estimation on PM2.5 distribution in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, and meteorological documents. Firstly, the 3 km and 10 km MODIS AOD products were validated with AErosol RObotic NETwork (AERONET AOD. Then the MLR and GWR models were employed respectively to estimate PM2.5 concentrations using ground measurements and two MODIS AOD products, meteorological datasets and land use information. Seasonal and regional analyses were also followed to make a comparative study on strengths and weaknesses between the 3 km and 10 km AOD products. Finally, the number of non-accidental deaths attributed to the long-term exposure of PM2.5 in the BTH region was estimated spatially. The results demonstrated that the 10 km AOD product provided results with a higher accuracy and greater coverage, although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimation. Additionally, compared with the global regression, the geographically weighed regression model was able to improve the estimation results. Finally, it was estimated that more than 30,000 people died in the BTH region during the study period attributed to the excessive PM2.5 concentrations

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest

Half century change of interactions among ecosystem services driven by ecological restoration: Quantification and policy implications at a watershed scale in the Chinese Loess Plateau

The concept of Ecosystem Service (ES) has provided an underpinning framework for ecological restoration research and applications. Ecological restoration is a corrective intervention that aims to reverse land degradation and to contribute to the 2030 Global Sustainable Development goal of Land Degradation Neutrality. It is critical to investigate the long-term effects of ecological restoration and land use change on ESs and ES interactions (synergies or trade-offs) to better understand the mechanisms supporting this goal. This paper describes an analysis of land use and ESs using historical data for a typical watershed in Chinese Loess Plateau, which has experienced series of restoration activities since the 1950s. Six important ESs (food provisioning, soil retention, hydrological regulation, carbon sequestration, water purification and habitat provisioning for biodiversity) were quantified at eight intervals between 1958 and 2015. The interactions between ESs were evaluated by correlation analysis. The results show that soil retention, carbon sequestration, water purification and habitat provisioning for biodiversity increased significantly across the different land use types over several decades but not hydrological regulation. The relationship between ESs was found to be variable over different time periods and a transition point between 1990 and 1995 was identified. Grassland was found to maintain greater water yield than woodland with high values of other ESs. The results suggest that trade-offs between ESs can be mitigated by adjusting the proportion of some important land use types (such as woodland and grassland)

Sustainable Use of Soils and Water: The Role of Environmental Land Use Conflicts

This book on the sustainable use of soils and water addressed a variety of issues related to the utopian desire for environmental sustainability and the deviations from this scene observed in the real world. Competing interests for land are frequently a factor in land degradation, especially where the adopted land uses do not conform with the land capability (the natural use of soil). The concerns of researchers about these matters are presented in the articles comprising this Special Issue book. Various approaches were used to assess the (im)balance between economic profit and environmental conservation in various regions, in addition to potential routes to bring landscapes back to a sustainable status being disclosed

Investigating tropospheric and surface ozone sensitivity from present day to future

Tropospheric ozone (O3) is an important reactive gas in the atmosphere influencing human health, ecosystems and climate. Since the mid-20th century, scientists started to explore the mechanism of tropospheric O3 formation after severe O3 air pollution in Los Angeles. They found that O3 is a photochemical pollutant as its formation involves energy from sunlight, as well as precursors nitrogen oxide (NOx), volatile organic compounds (VOCs) and carbon monoxide (CO). Nowadays, highly O3 polluted episodes can still occur in areas where emissions have been controlled strictly due to the non-linear chemical reactions of O3 formation. Therefore, it is important to implement suitable emission control strategies to mitigate O3 pollution, and to understand the impacts of emissions and climate on O3 changes in the future. Firstly, a chemistry scheme with more reactive VOC species is developed based on the Strat-Trop chemistry scheme in the United Kingdom Earth System Model, UKESM1. This permits a more realistic and photochemically active environment for O3 simulation in areas with high reactive VOC emissions. The effectiveness of emission controls in reducing surface O3 concentrations in the industrial regions of China in summer, 2016, is investi gated. The concentrations of surface O3 in those regions generally can be simulated accurately, and the diurnal variation of O3 can also be captured well by the model. O3 production in most regions is VOC-limited, suggesting that surface O3 concentrations will increase as NOx emissions decrease. In the VOC-limited regions, more than 70 % reductions in NOx emissions alone are required to reduce surface O3 concentrations. Reductions in 20 % VOC emissions alone lead to 11 % decreases in surface O3 concentrations, and are effective in offsetting increased O3 levels that would otherwise occur through decreased NOx emissions alone. Subsequently, the evolution of tropospheric O3 from the present day (2004- 2014) to the future (2045-2055) under the shared socio-economic pathways (SSPs) is investigated to demonstrate the impacts of different climate and emissions on O3 changes. In the context of climate change, changes in the tropospheric O3 burden in the future can be largely explained by changes in O3 precursor emissions. However, surface O3 changes vary substantially by season in high-emission regions due to different seasonal O3 sensitivity. VOC-limited areas are more extensive in winter (7 %) than in summer (3 %) across the globe. Reductions in NOx emissions are the key to transform O3 production from a VOC- to NOx-limited chemical environment, but will lead to increased O3 concentrations in high-emission regions, and hence emission controls on VOC and methane (CH4) are also necessary. Lastly, a deep learning model is developed to demonstrate the feasibility of correcting surface O3 biases in UKESM1, to identify key processes causing them, and to correct projections of future surface O3. Temperature and related geographic variables latitude and month show the strongest relationship with O3 biases. This indicates that O3 biases are sensitive to temperature and suggests weakness in representation of temperature-sensitive physical or chemical processes. Photolysis rates are also shown to be important for O3 biases likely due to uncertainties in cloud cover and insolation simulations. Chemical species such as the hydroxyl radical, nitric acid and peroxyacyl nitrates show a clear relationship to O3 biases, associated with uncertainties in emissions, chemical production and destruction, and deposition. Corrected seasonal O3 changes are generally smaller than those simulated with UKESM1 in high-emission regions. This demonstrates that O3 sensitivity to future emissions and climate in UKESM1 may be stronger than that in the real atmosphere. Given the uncertainty in simulating future ozone, we show that deep learning approaches can provide improved assessment of the impacts of climate and emission changes on future air quality, along with valuable information to guide future model development. The work presented here offers a valuable assessment of emission control strategies to resolve current O3 air pollution problems in China, and also quantifies the changes in the tropospheric O3 burden and global surface O3 sensitivity in the future under different emission and climate scenarios. Deep learning guides possible directions to improve model performance in surface O3 simulations for a global chemistry-climate model, and provides more accurate projections of O3 pollution in the future

Evaluating the Use of Environmental Tracers to Reduce Conceptual Model Uncertainty of Hydrogeologic Models

Environmental tracer concentrations for CFC12, SF6, and tritium are used in groundwater simulations to assess the ability of these tracers to reduce conceptual model uncertainty due to uncertainty of a site’s geologic and recharge characterization. The resulting groundwater simulations are characterized by site-specific hydrologic and geologic data, and with coordination from a field team with years of knowledge about the site. First-order (conceptual) uncertainty is directly addressed by using a stochastic modeling approach for spatial variability of the proposed subsurface configurations. Simulations of environmental tracer concentrations and water levels are used to assess six alternate conceptual models that are based on three alternate geologic interpretations and two levels of spatial complexity in groundwater recharge. Our results show that water levels and tracers both provide unique information, but tracers enhance our ability to distinguish between models throughout multiple analyses. Tracers CFC12 and tritium show how simulating environmental tracer transport in groundwater is better than using water levels at testing alternate hydrogeologic conceptual models and reducing conceptual uncertainty between them

Land Change Science and the STEPLand Framework : An Assessment of Its Progress

This contribution assesses a new term that is proposed to be established within Land Change Science: Spatio-TEmporal Patterns of Land ('STEPLand'). It refers to a specific workflow for analyzing land-use/land cover (LUC) patterns, identifying and modeling driving forces of LUC changes, assessing socio-environmental consequences, and contributing to defining future scenarios of land transformations. In this article, we define this framework based on a comprehensive meta-analysis of 250 selected articles published in international scientific journals from 2000 to 2019. The empirical results demonstrate that STEPLand is a consolidated protocol applied globally, and the large diversity of journals, disciplines, and countries involved shows that it is becoming ubiquitous. In this paper, the main characteristics of STEPLand are provided and discussed, demonstrating that the operational procedure can facilitate the interaction among researchers from different fields, and communication between researchers and policy makers

The Kobresia pygmaea ecosystem of the Tibetan highlands – Origin, functioning and degradation of the world's largest pastoral alpine ecosystem: Kobresia pastures of Tibet

With 450,000 km2 Kobresia (syn. Carex) pygmaea dominated pastures in the eastern Tibetan highlands are the world's largest pastoral alpine ecosystem forming a durable turf cover at 3000–6000 m a.s.l. Kobresia's resilience and competitiveness is based on dwarf habit, predominantly below-ground allocation of photo assimilates, mixture of seed production and clonal growth, and high genetic diversity. Kobresia growth is co-limited by livestock-mediated nutrient withdrawal and, in the drier parts of the plateau, low rainfall during the short and cold growing season. Overstocking has caused pasture degradation and soil deterioration over most parts of the Tibetan highlands and is the basis for this man-made ecosystem. Natural autocyclic processes of turf destruction and soil erosion are initiated through polygonal turf cover cracking, and accelerated by soil-dwelling endemic small mammals in the absence of predators. The major consequences of vegetation cover deterioration include the release of large amounts of C, earlier diurnal formation of clouds, and decreased surface temperatures. These effects decrease the recovery potential of Kobresia pastures and make them more vulnerable to anthropogenic pressure and climate change. Traditional migratory rangeland management was sustainable over millennia, and possibly still offers the best strategy to conserve and possibly increase C stocks in the Kobresia turf. © 201