Mengmeng Hao

Spatial-temporal variation of marginal land suitable for energy plants from 1990 to 2010 in China

Energy plants are the main source of bioenergy which will play an increasingly important role in future energy supplies. With limited cultivated land resources in China, the development of energy plants may primarily rely on the marginal land. In this study, based on the land use data from 1990 to 2010(every 5 years is a period) and other auxiliary data, the distribution of marginal land suitable for energy plants was determined using multi-factors integrated assessment method. The variation of land use type and spatial distribution of marginal land suitable for energy plants of different decades were analyzed. The results indicate that the total amount of marginal land suitable for energy plants decreased from 136.501 million ha to 114.225 million ha from 1990 to 2010. The reduced land use types are primarily shrub land, sparse forest land, moderate dense grassland and sparse grassland, and large variation areas are located in Guangxi, Tibet, Heilongjiang, Xinjiang and Inner Mongolia. The results of this study will provide more effective data reference and decision making support for the long-term planning of bioenergy resources.

Mapping the spatial distribution of Aedes aegypti and Aedes albopictus

Mosquito-borne infectious diseases, such as Rift Valley fever, Dengue, Chikungunya and Zika, have caused mass human death with the transnational expansion fueled by economic globalization. Simulating the distribution of the disease vectors is of great importance in formulating public health planning and disease control strategies. In the present study, we simulated the global distribution of Aedes aegypti and Aedes albopictus at a 5×5km spatial resolution with high-dimensional multidisciplinary datasets and machine learning methods Three relatively popular and robust machine learning models, including support vector machine (SVM), gradient boosting machine (GBM) and random forest (RF), were used. During the fine-tuning process based on training datasets of A. aegypti and A. albopictus, RF models achieved the highest performance with an area under the curve (AUC) of 0.973 and 0.974, respectively, followed by GBM (AUC of 0.971 and 0.972, respectively) and SVM (AUC of 0.963 and 0.964, respectively) models. The simulation difference between RF and GBM models was not statistically significant (p>0.05) based on the validation datasets, whereas statistically significant differences (p<0.05) were observed for RF and GBM simulations compared with SVM simulations. From the simulated maps derived from RF models, we observed that the distribution of A. albopictus was wider than that of A. aegypti along a latitudinal gradient. The discriminatory power of each factor in simulating the global distribution of the two species was also analyzed. Our results provided fundamental information for further study on disease transmission simulation and risk assessment.

Could biofuel development stress China's water resources?

Concerns over energy shortages and global climate change have stimulated developments toward renewable energy. Biofuels have been developed to replace fossil fuels to reduce the emissions of greenhouse gases and other environmental impacts. However, food security and water scarcity are other growing concerns, and the increased production of biofuels may increase these problems. This study focuses on whether biofuel development would stress Chinas water resources. Cassava‐based fuel ethanol and sweet sorghum‐based fuel ethanol are the focus of this study because they are the most typical nongrain biofuels in China. The spatial distribution of the total water requirement of fuel ethanol over its life cycle process was simulated using a biophysical biogeochemical model and marginal land as one of the types of input data for the model to avoid impacts on food security. The total water requirement of fuel ethanol was then compared with the spatial distribution of water resources, and the influence of the development of fuel ethanol on water resources at the pixel and river basin region scales was analyzed. The result showed that the total water requirement of fuel ethanol ranges from 37.81 to 862.29 mm. However, considering water resource restrictions, not all of the marginal land is suitable for the development of fuel ethanol. Approximately 0.664 million km2 of marginal land is suitable for the development of fuel ethanol, most of which is located in the south of China, where water resources are plentiful. For these areas, the value of fuel ethanols water footprint ranges from 0.05 to 11.90 m3 MJ−1. From the water point of view, Liaoning province, Guizhou province, Anhui province and Hunan province can be given priority for the development of fuel ethanol.

Evaluating the bioenergy potential of cassava on marginal land using a biogeochemical process model in GuangXi, China

Abstract. Bioenergy is expected to play an important role in future energy systems. Cassava is believed to be one of the most promising energy plants for fuel ethanol production in the tropics and subtropics. In China, plant-based bioenergy has to be developed on marginal land to avoid impacting food security. Cassava yield varies dramatically under different environmental conditions. Therefore, an efficient approach is needed to estimate cassava yield on marginal land. This paper presents a method for assessing the energy potential of cassava using a biogeochemical process model. First, the spatial distribution of marginal land was identified. A geographic information system-based biogeochemical process model, the GIS-based environmental policy integrated climate model, was used to simulate the spatial and temporal dynamics of the major processes of the soil-cassava-atmosphere management system. The model was calibrated and successfully applied to data from GuangXi province, Southwest China. The results indicated that the potential bioenergy of cassava on marginal land under rain-fed conditions in GuangXi province is 1,909,593.96 million MJ, which is equivalent to the energy of 17.0844 million tons of standard coal; the potential energy of irrigated cassava is 2,054,017.73 million MJ, which is equivalent to the energy of 18.3765 million tons of standard coal.

Assessment of the GHG Reduction Potential from Energy Crops Using a Combined LCA and Biogeochemical Process Models: A Review

The main purpose for developing biofuel is to reduce GHG (greenhouse gas) emissions, but the comprehensive environmental impact of such fuels is not clear. Life cycle analysis (LCA), as a complete comprehensive analysis method, has been widely used in bioenergy assessment studies. Great efforts have been directed toward establishing an efficient method for comprehensively estimating the greenhouse gas (GHG) emission reduction potential from the large-scale cultivation of energy plants by combining LCA with ecosystem/biogeochemical process models. LCA presents a general framework for evaluating the energy consumption and GHG emission from energy crop planting, yield acquisition, production, product use, and postprocessing. Meanwhile, ecosystem/biogeochemical process models are adopted to simulate the fluxes and storage of energy, water, carbon, and nitrogen in the soil-plant (energy crops) soil continuum. Although clear progress has been made in recent years, some problems still exist in current studies and should be addressed. This paper reviews the state-of-the-art method for estimating GHG emission reduction through developing energy crops and introduces in detail a new approach for assessing GHG emission reduction by combining LCA with biogeochemical process models. The main achievements of this study along with the problems in current studies are described and discussed.

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.

Estimating the potential of energy saving and carbon emission mitigation of cassava-based fuel ethanol using life cycle assessment coupled with a biogeochemical process model

Global warming and increasing concentration of atmospheric greenhouse gas (GHG) have prompted considerable interest in the potential role of energy plant biomass. Cassava-based fuel ethanol is one of the most important bioenergy and has attracted much attention in both developed and developing countries. However, the development of cassava-based fuel ethanol is still faced with many uncertainties, including raw material supply, net energy potential, and carbon emission mitigation potential. Thus, an accurate estimation of these issues is urgently needed. This study provides an approach to estimate energy saving and carbon emission mitigation potentials of cassava-based fuel ethanol through LCA (life cycle assessment) coupled with a biogeochemical process model—GEPIC (GIS-based environmental policy integrated climate) model. The results indicate that the total potential of cassava yield on marginal land in China is 52.51 million t; the energy ratio value varies from 0.07 to 1.44, and the net energy surplus of cassava-based fuel ethanol in China is 92,920.58 million MJ. The total carbon emission mitigation from cassava-based fuel ethanol in China is 4593.89 million kgC. Guangxi, Guangdong, and Fujian are identified as target regions for large-scale development of cassava-based fuel ethanol industry. These results can provide an operational approach and fundamental data for scientific research and energy planning.

Mapping the Potential Global Codling Moth ( Cydia pomonella L .) Distribution Based on a Machine Learning Method

The spread of invasive species may pose great threats to the economy and ecology of a region. The codling moth (Cydia pomonella L.) is one of the 100 worst invasive alien species in the world and is the most destructive apple pest. The economic losses caused by codling moths are immeasurable. It is essential to understand the potential distribution of codling moths to reduce the risks of codling moth establishment. In this study, we adopted the Maxent (Maximum Entropy Model), a machine learning method to predict the potential global distribution of codling moths with global accessibility data, apple yield data, elevation data and 19 bioclimatic variables, considering the ecological characteristics and the spread channels that cover the processes from growth and survival to the dispersion of the codling moth. The results show that the areas that are suitable for codling moth are mainly distributed in Europe, Asia and North America, and these results strongly conformed with the currently known occurrence regions. In addition, global accessibility, mean temperature of the coldest quarter, precipitation of the driest month, annual mean temperature and apple yield were the most important environmental predictors associated with the global distribution of codling moths.

Mapping the transmission risk of Zika virus using machine learning models

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expansion fueled by increased global travel and trade. Suitability mapping of the transmission risk of Zika virus is essential for drafting public health plans and disease control strategies, which are especially important in areas where medical resources are relatively scarce. Predicting the risk of Zika virus outbreak has been studied in recent years, but the published literature rarely includes multiple model comparisons or predictive uncertainty analysis. Here, three relatively popular machine learning models including backward propagation neural network (BPNN), gradient boosting machine (GBM) and random forest (RF) were adopted to map the probability of Zika epidemic outbreak at the global level, pairing high-dimensional multidisciplinary covariate layers with comprehensive location data on recorded Zika virus infection in humans. The results show that the predicted high-risk areas for Zika transmission are concentrated in four regions: Southeastern North America, Eastern South America, Central Africa and Eastern Asia. To evaluate the performance of machine learning models, the 50 modeling processes were conducted based on a training dataset. The BPNN model obtained the highest predictive accuracy with a 10-fold cross-validation area under the curve (AUC) of 0.966 [95% confidence interval (CI) 0.965-0.967], followed by the GBM model (10-fold cross-validation AUC = 0.964[0.963-0.965]) and the RF model (10-fold cross-validation AUC = 0.963[0.962-0.964]). Based on training samples, compared with the BPNN-based model, we find that significant differences (p = 0.0258* and p = 0.0001***, respectively) are observed for prediction accuracies achieved by the GBM and RF models. Importantly, the prediction uncertainty introduced by the selection of absence data was quantified and could provide more accurate fundamental and scientific information for further study on disease transmission prediction and risk assessment.

Monitoring the Coastal Environment Using Remote Sensing and GIS Techniques

The coastal zone has been of importance for economic development and ecological restoration due to their rich natural resources and vulnerable ecosystems. Remote sensing techniques have proven to be powerful tools for the monitoring of the Earth’s surface and atmosphere on a global, regional, and even local scale, by pro‐ viding important coverage, mapping and classification of land cover features such as vegetation, soil, water and forests. This chapter introduced the methods for mon‐ itoring the coastal environment using remote sensing and GIS techniques. Case studies of port expansion monitoring in typical coastal regions, together with the coastal environment changes analysis were also presented. Coastal zones are important for economic development and ecological restoration due to their rich natural resources and vulnerable ecosystems. Remote sensing tech‐ niques have been shown to be powerful tools in the monitoring of the Earth’s sur‐ face and atmosphere on global, regional, and local scales. These techniques provide important coverage and mapping and help classify land cover, such as vegetation, soil, water, and forests. The main objectives of this chapter are (1) to introduce the state-of-the-art techniques on monitoring coastal environments using remote sens‐ ing and geographic information systems (GIS), including landscape classification, automatic classification and change detection on regional scale, and objective-based classification on local scale, and (2) to present case studies of coastal environment monitoring, including Zhan Jiang Port in the stage of specialization, Gwadar Port and Djibouti Port in the expansion stage, and Ilichevsk Port in the stable stage.