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

UAV Near Infrared Remote Sensing for Crop Growth Monitoring

Hutong Fan, GEG 518: Remote Sensing Faculty Mentors: Professor Tao Tang, Geography and Planning and Professor Lei Deng, Capital Normal University Nowadays, precision agriculture becomes more and more important in agricultural production. Remote sensing techniques are always used in precision agriculture to promote the productivity of crops. Although satellite-based remote sensing has been a popular method for monitoring the earth\u27s surface, it has several drawbacks. With the improvement of drone technology, an unmanned air vehicle (UAV) is more flexible in terms of deployment, monitoring small areas, and cost effective on data collections. In this project, the first objective is to merge the UAV images of the vineyard. There are two kinds of images, RGB and near-infrared. For the near-infrared images which cannot be merged automatically using Pix4D photogrammetric software, Photoshop software was used to conduct the preliminary synthesis. The second objective is to combine both RGB and infrared images into different layers radiation bands of one image and to use NDVI (Normalized Difference Vegetation Index) to analyze the growth situation of grapes in the study vineyard. This research demonstrated that it is feasible to use Photoshop software to mosaic the images that cannot be merged in Pix4D. However, further studies are need for the accuracy issues of both spectral and locations. The significance of this study is 1) finding the alternative method to merge the near-infrared images, and 2) using the NDVI to analyze the growth conditions of the grapes in the study area.https://digitalcommons.buffalostate.edu/srcc-sp20-physgeosci/1003/thumbnail.jp