School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science & Technology, Nanjing 210044, China; School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Chemosphere. 2021 May;271:129584. doi: 10.1016/j.chemosphere.2021.129584. Epub 2021 Jan 10.
Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM concentration (μgm), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen.
有关可吸入悬浮颗粒物污染物浓度的时空变化信息，尤其是 2.5μm 大小的颗粒物以及气候，是与全球范围内新出现的 COVID-19 病例相关的重要因素。本研究旨在利用地理空间技术，在巴基斯坦的三个省会城市和联邦首都检查 COVID-19 病例、空气污染、气候和社会经济因素之间的关联。从 2020 年 6 月 1 日至 7 月 31 日，从巴基斯坦的 4 个省中的 3 个（旁遮普邦、信德省、开伯尔-普赫图赫瓦省（KPK））获取了一系列相关数据，包括 COVID-19 病例的每日数量、PM 浓度（μg/m3）、气候因素，包括温度（°F）、风速（m/s）、湿度（%）、露点（%）和压力（Hg）。此外，还确定了人口密度与 COVID-19 病例之间的可能关系。采用广义线性模型（GLM）来量化 PM、温度、露点、湿度、风速和压力范围对每日 COVID-19 病例的影响。还实施了灰色关联分析（GRA）来检查省级城市拉合尔的 COVID-19 病例随 PM 浓度的变化。在研究期间，旁遮普邦、信德省、开伯尔-普赫图赫瓦省和伊斯兰堡报告了约 1,92,819 例 COVID-19 病例。结果表明，除拉合尔湿度（r=0.175）外，COVID-19 病例与 PM 和气候因素之间存在显著关系，p<0.05。然而，拉合尔、卡拉奇、白沙瓦和伊斯兰堡之间存在混合相关性。R 值表明 COVID-19 与人口密度之间存在中度关系。尽管本研究结果是初步的，但为流行病学家提供了第一手证据，并可能有助于当地社区制定有效的 COVID-19 感染和健康风险管理指南。这还有待观察。
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