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气象因素和地形对空气污染浓度和迁移的影响:来自波兰克拉科夫的地质统计学案例研究。

The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland.

机构信息

Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Adama Mickiewicza 30, 30-059, Kraków, Malopolska, Poland.

出版信息

Sci Rep. 2022 Jun 30;12(1):11050. doi: 10.1038/s41598-022-15160-3.

DOI:10.1038/s41598-022-15160-3
PMID:35773386
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9244891/
Abstract

Despite the very restrictive laws, Krakow is known as the city with the highest level of air pollution in Europe. It has been proven that, due to its location, air pollutants are transported to this city from neighboring municipalities. In this study, a complex geostatistical approach for spatio-temporal analysis of particulate matter (PM) concentrations was applied. For background noise reduction, data were recorded during the COVID-19 lockdown using 100 low-cost sensors and were validated based on indications from reference stations. Standardized Geographically Weighted Regression, local Moran's I spatial autocorrelation analysis, and Getis-Ord Gi* statistic for hot-spot detection with Kernel Density Estimation maps were used. The results indicate the relation between the topography, meteorological variables, and PM concentrations. The main factors are wind speed (even if relatively low) and terrain elevation. The study of the PM2.5/PM10 ratio allowed for a detailed analysis of spatial pollution migration, including source differentiation. This research indicates that Krakow's unfavorable location makes it prone to accumulating pollutants from its neighborhood. The main source of air pollution in the investigated period is solid fuel heating outside the city. The study shows the importance and variability of the analyzed factors' influence on air pollution inflow and outflow from the city.

摘要

尽管法律非常严格,但克拉科夫被认为是欧洲空气污染水平最高的城市。事实证明,由于其地理位置,空气污染物会从邻近的城市输送到这个城市。在这项研究中,应用了一种复杂的时空分析颗粒物(PM)浓度的地质统计学方法。为了降低背景噪声,在 COVID-19 封锁期间使用 100 个低成本传感器记录数据,并基于参考站的指示进行验证。使用标准化地理加权回归、局部 Moran's I 空间自相关分析和 Getis-Ord Gi* 统计来检测热点,并结合核密度估计图。结果表明了地形、气象变量和 PM 浓度之间的关系。主要因素是风速(即使相对较低)和地形高程。PM2.5/PM10 比值的研究允许对空间污染迁移进行详细分析,包括源区分。这项研究表明,克拉科夫不利的地理位置使其容易受到附近地区污染物的积累。在研究期间,空气污染的主要来源是城市外的固体燃料加热。研究表明,分析因素对城市内外空气污染流入和流出的影响的重要性和可变性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/2c63f092ba50/41598_2022_15160_Fig13_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/d8479aae29f6/41598_2022_15160_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/f705ec0ff4eb/41598_2022_15160_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/134cbaff8769/41598_2022_15160_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/74087f69ded4/41598_2022_15160_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/a3c9f7811beb/41598_2022_15160_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/d00ab08c5248/41598_2022_15160_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/647774ce5dc1/41598_2022_15160_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/cfe6271b8fb4/41598_2022_15160_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/44356b537ea2/41598_2022_15160_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/52c3db8b6e1e/41598_2022_15160_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/493ba7877361/41598_2022_15160_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5451/9247078/2c63f092ba50/41598_2022_15160_Fig13_HTML.jpg

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