In order to find effective ways to control atmospheric environment, this paper mainly studies the Spatial-temporal features of atmospheric pollutant distribution in the past 2014-2017 years. The particulate matter concentration was retrieved by remote sensing data and all data would be processed by Kriging method. The results show that most pollutant concentration decreased, especially SO2, PM2.5 and PM10, though the ozone problem is beginning to stand out; In addition, CO concentration in different regions varies in different seasons, meanwhile other pollutants do not like this.
| Published in | Science Discovery (Volume 7, Issue 2) |
| DOI | 10.11648/j.sd.20190702.18 |
| Page(s) | 98-106 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Atmospheric Pollutant, Inversion, Kriging, Remote Sensing Data, Spatial-Temporal Features
| [1] | 严心田,李楠,余欢,等.典型会议期间污染调控对城市空气质量影响分析[J].南京理工大学学报,2018,42(06):739-746. |
| [2] | 王景云,张红日,赵相伟,等.2012—2015年北京市空气质量指数变化及其与气象要素的关系[J].气象与环境科学,2017,40(04):35-41. |
| [3] | Hoff, Raymond M,Christopher, Sundar A. Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? [J]. Journal of the Air & Waste Management Association, 2009, 59(6). |
| [4] | Randall V. Martin. Satellite remote sensing of surface air quality [J]. Atmospheric Environment, 2008, 42(34). |
| [5] | Wang J, Christopher S A. Intercomparison between satellite -derived aerosol optical thickness and PM2.5 mass: Implicationsfor air quality studies[J]. Geophysical Research Letters, 2003,30(21): 267-283. |
| [6] | Donkelaar A V, Martin R V, Brauer M et al. Global estimates of ambient fine particulate matter concentrations from Satel-lite-based Aerosol Optical Depth: Development and application [J]. Environmental Health Perspectives, 2010, 118(6): 847-855. |
| [7] | 赵柏林,张芃菲,高国明.我国大气气溶胶光学厚度的特性[J].气象学报,1986,44(2):235-241. |
| [8] | 刘大锰,黄杰,高少鹏,等.北京市区春季交通源大气颗粒 物的污染水平及其影响因素[J].地学前缘,2006,13(2):228-233. |
| [9] | 顾吉林，汤宏山，刘淼，等．大连市大气污染物质量浓度与气溶胶光学厚度的相关性分析[J/OL].地理科学. http://kns.cnki.net/kcms/detail/22.1124.P.20190325.0959.002.html |
| [10] | 李慧杰,王秀兰,王计平,李梦捷,杨晓潇.2013—2017年间京津冀地区空气质量及影响因素分析[J].环境监测管理与技术,2019, 3:1-6. |
| [11] | 王斌.利用空气污染指数(API)分析我国空气污染的区域时空变化特征[D].青岛:海洋大学,2008. |
| [12] | 李祥,彭玲,池天河,李浩川,徐逸之.北京市空气质量时空特征分析[J].测绘通报,2016(09):47-51. |
| [13] | 邓岳川,谷双喜.克里金插值的多路径误差空间分布特征[J].测绘科学,2018,43(04):17-23. |
| [14] | 周体鹏. 基于克里金插值法的昆明市PM2.5预测[D].昆明：云南大学,2016. |
APA Style
Tang Xiru, Xu Liping, Liu Shufu, Shen Chunming, Wang Bin, et al. (2019). Features of Atmospheric Pollutant in Beijing Region from 2014 to 2017. Science Discovery, 7(2), 98-106. https://doi.org/10.11648/j.sd.20190702.18
ACS Style
Tang Xiru; Xu Liping; Liu Shufu; Shen Chunming; Wang Bin, et al. Features of Atmospheric Pollutant in Beijing Region from 2014 to 2017. Sci. Discov. 2019, 7(2), 98-106. doi: 10.11648/j.sd.20190702.18
AMA Style
Tang Xiru, Xu Liping, Liu Shufu, Shen Chunming, Wang Bin, et al. Features of Atmospheric Pollutant in Beijing Region from 2014 to 2017. Sci Discov. 2019;7(2):98-106. doi: 10.11648/j.sd.20190702.18
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Download@article{10.11648/j.sd.20190702.18,
author = {Tang Xiru and Xu Liping and Liu Shufu and Shen Chunming and Wang Bin and Long Tao},
title = {Features of Atmospheric Pollutant in Beijing Region from 2014 to 2017},
journal = {Science Discovery},
volume = {7},
number = {2},
pages = {98-106},
doi = {10.11648/j.sd.20190702.18},
url = {https://doi.org/10.11648/j.sd.20190702.18},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20190702.18},
abstract = {In order to find effective ways to control atmospheric environment, this paper mainly studies the Spatial-temporal features of atmospheric pollutant distribution in the past 2014-2017 years. The particulate matter concentration was retrieved by remote sensing data and all data would be processed by Kriging method. The results show that most pollutant concentration decreased, especially SO2, PM2.5 and PM10, though the ozone problem is beginning to stand out; In addition, CO concentration in different regions varies in different seasons, meanwhile other pollutants do not like this.},
year = {2019}
}
TY - JOUR T1 - Features of Atmospheric Pollutant in Beijing Region from 2014 to 2017 AU - Tang Xiru AU - Xu Liping AU - Liu Shufu AU - Shen Chunming AU - Wang Bin AU - Long Tao Y1 - 2019/05/23 PY - 2019 N1 - https://doi.org/10.11648/j.sd.20190702.18 DO - 10.11648/j.sd.20190702.18 T2 - Science Discovery JF - Science Discovery JO - Science Discovery SP - 98 EP - 106 PB - Science Publishing Group SN - 2331-0650 UR - https://doi.org/10.11648/j.sd.20190702.18 AB - In order to find effective ways to control atmospheric environment, this paper mainly studies the Spatial-temporal features of atmospheric pollutant distribution in the past 2014-2017 years. The particulate matter concentration was retrieved by remote sensing data and all data would be processed by Kriging method. The results show that most pollutant concentration decreased, especially SO2, PM2.5 and PM10, though the ozone problem is beginning to stand out; In addition, CO concentration in different regions varies in different seasons, meanwhile other pollutants do not like this. VL - 7 IS - 2 ER -
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