Spatial and Temporal Analysis of Big Dataset on PM2.5 Air Pollution in Beijing, China, 2014 to 2018

Air particulate matter (PM2.5) pollution is a critical environment problem worldwide and also in Beijing, China. We gathered five-year PM2.5 contaminate concentrations from 2014 to 2018, from the Beijing Municipal Environmental Monitoring Center and China Air Quality Real-time Distribution Platform. This is a big dataset, and we collected with crawler technology from Python programming. After examining the quality of the recorded data, we determined to conduct the temporal and spatial analysis using 27 observation stations located in both urban and suburb area in the municipality of Beijing. The big dataset of five-year hourly PM2.5 concentrations was sorted to actionable datasets (Selected Datasets and Seasonal Average Selected Datasets) with the help of Python programming. Linear Regression based Fundamental Data Analysis was conducted as the first part of temporal analysis in R studio to gather the temporal patterns of five-year seasonal PM2.5 contaminant concentrations on each observation sites. As the second part of temporal analysis, the Principal Component Analysis (PCA) was conducted in MATLAB to gather the patterns of variations of entire five-year PM2.5 contaminant concentration on each of the sites. Geographic Information System (GIS) was utilized to study the spatial pattern of air pollution distribution from the selected 27 observation sites during selected time periods. The results of this research are, 1) PM2.5 pollutions in winter are the most severe or the highest in each of the natural years. 2) PM2.5 pollution concentrations in Beijing were gradually decrease during 2014 to 2018. 3) In terms of a five-year time perspective, the improvements of air quality and reduction of PM2.5 contaminant appeared in all the seasons based on Fundamental Data Analysis. 4) PM2.5 contaminant concentrations in summer are significantly less than other seasons. 5) The least PM2.5 pollutant influenced area is north and northwest regions in Beijing, and the most PM2.5 pollutant influenced area is south and southeast areas in Beijing. 6) Vehicle concentration and traffic congestion is not the significant impact factor of PM2.5 pollutions in Beijing. 7) Heating supply of buildings and houses generated great contributions to the PM2.5 contaminant concentration in Beijing. While, in the background of rigorous emission reduction policy and management operations by the municipal government, contribution of heating supplies is gradually decreasing. 8) Human activities have limited contributions to the PM2.5 contaminants in Beijing. Meanwhile, type and quantity of fossil fuel energy consumptions might contribute large amount of air pollutions

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

The Drought Risk Analysis, Forecasting, and Assessment under Climate Change

This Special Issue is a platform to fill the gaps in drought risk analysis with field experience and expertise. It covers (1) robust index development for effective drought monitoring; (2) risk analysis framework development and early warning systems; (3) impact investigations on hydrological and agricultural sectors; (4) environmental change impact analyses. The articles in the Special Issue cover a wide geographic range, across China, Taiwan, Korea, and the Indo-China peninsula, which covers many contrasting climate conditions. Hence, the results have global implications: the data, analysis/modeling, methodologies, and conclusions lay a solid foundation for enhancing our scientific knowledge of drought mechanisms and relationships to various environmental conditions

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes

Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decisionmaking procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils. The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from January 1, 2007 up to April 30, 2018. Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation

Application of different watershed units to debris flow susceptibility mapping: A case study of Northeast China

The main purpose of this study was to compare two types of watershed units divided by the hydrological analysis method (HWUs) and mean curvature method (CWUs) for debris flow susceptibility mapping (DFSM) in Northeast China. Firstly, a debris flow inventory map consisting of 129 debris flows and 129 non-debris flows was randomly divided into a ratio of 70% and 30% for training and testing. Secondly, 13 influencing factors were selected and the correlations between these factors and the debris flows were determined by frequency ration analysis. Then, two types of watershed units (HWUs and CWUs) were divided and logistic regression (LR), multilayer perceptron (MLP), classification and regression tree (CART) and Bayesian network (BN) were selected as the evaluation models. Finally, the predictive capabilities of the models were verified using the predictive accuracy (ACC), the Kappa coefficient and the area under the receiver operating characteristic curve (AUC). The mean AUC, ACC and Kappa of four models (LR, MLP, CART and BN) in the training stage were 0.977, 0.931, and 0.861, respectively, for the HWUs, while 0.961, 0.905, and 0.810, respectively, for the CWUs; in the testing stage, were 0.904, 0.818, and 0.635, respectively, for the HWUs, while 0.883, 0.800, and 0.601, respectively, for the CWUs, which showed that HWU model has a higher debris flow prediction performance compared with the CWU model. The CWU-based model can reflect the spatial distribution probability of debris flows in the study area overall and can be used as an alternative model

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Land Use Change from Non-urban to Urban Areas

This reprint is related to land-use change and non-urban and urban relationships at all spatiotemporal scales and also focuses on land-use planning and regulatory strategies for a sustainable future. Spatiotemporal dynamics, socioeconomic implication, water supply problems and deforestation land degradation (e.g., increase of imperviousness surfaces) produced by urban expansion and their resource requirements are of particular interest. The Guest Editors expect that this reprint will contribute to sustainable development in non-urban and urban areas