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利用地理空间技术评估德里地区的空气质量:COVID期间(2018 - 2023年)污染趋势的比较分析

Assessing Delhi Region's air quality using geospatial technologies: a comparative analysis of pollution trends during the COVID period (2018-2023).

作者信息

Thakur Shubham, Tangri Amanpreet, Singh Kanwarpreet, Sharma Sahil

机构信息

Civil Engineering Department, Chandigarh University, Mohali, Punjab, India.

University Centre for Research and Development, Chandigarh University, Mohali, Punjab, India.

出版信息

Environ Monit Assess. 2025 Jun 3;197(7):722. doi: 10.1007/s10661-025-14177-1.

Abstract

This research examines trends in air quality in Delhi from 2018 to 2023 based on geospatial tools and statistical techniques, such as violin plot analysis, one-way analysis of variance (ANOVA), Tukey honestly significant difference (HSD), and Kriging interpolation. Air pollution measurements from 38 monitoring stations were evaluated to identify spatial and temporal patterns in the major pollutants (PM, PM, NO, SO, and ozone). The reports show a massive decline in the levels of PM from 242.91 µg/m in 2018 to 99.51 µg/m in 2022, with similar downward trends being reflected for PM (214.66 µg/m in 2019 to 106.88 µg/m in 2023) and NO (46.61 µg/m in 2018 to 14.99 µg/m in 2023). SO was comparatively static, with irregular industrial spikes, while the level of ozone varied, touching a high of 160 µg/m in some zones. COVID-19 lockdown contributed to a major decrease in levels of pollution, with PM and PM falling by more than 40%. Violin plot analysis showed fluctuations in pollutant concentrations between various regions, where localized changes were evident. One-way ANOVA and Tukey HSD tests also certified statistically significant variation in pollutant levels between varied phases, underlining the effects of lockdown practices. Spatial interpolation by Kriging resulted in high-resolution concentration maps, giving a complete picture of the distribution of pollution. The findings help deepen knowledge of air quality trends and guide policy interventions to reduce pollution and enhance public health in Delhi.

摘要

本研究基于地理空间工具和统计技术,如小提琴图分析、单因素方差分析(ANOVA)、图基诚实显著差异(HSD)和克里金插值法,研究了2018年至2023年德里的空气质量趋势。对来自38个监测站的空气污染测量数据进行了评估,以确定主要污染物(PM、PM、NO、SO和臭氧)的空间和时间模式。报告显示,PM水平从2018年的242.91µg/m大幅下降至2022年的99.51µg/m,PM(从2019年的214.66µg/m降至2023年的106.88µg/m)和NO(从2018年的46.61µg/m降至2023年的14.99µg/m)也呈现出类似的下降趋势。SO相对稳定,有不规则的工业峰值,而臭氧水平则有所变化,在某些区域高达160µg/m。新冠疫情封锁导致污染水平大幅下降,PM和PM下降超过40%。小提琴图分析显示了不同区域污染物浓度的波动,局部变化明显。单因素方差分析和图基HSD检验也证实了不同阶段污染物水平存在统计学上的显著差异,突出了封锁措施的影响。通过克里金法进行的空间插值生成了高分辨率浓度图,全面展示了污染分布情况。这些发现有助于加深对空气质量趋势的了解,并指导政策干预措施,以减少德里的污染并改善公众健康。

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