Spatiotemporal analysis of COVID-19, air pollution, climate, and meteorological conditions in a metropolitan region of Iran

Moazeni, Malihe; Maracy, Mohammad Reza; Dehdashti, Bahare; Ebrahimi, Afshin

doi:10.1007/s11356-021-17535-x

Cited by 14 publications

(5 citation statements)

References 67 publications

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“…The decline and changes of NO 2 , PM 2.5 and PM 10 have been observed in many countries from the first to the mid of Q2 of 2020 (15-May-2020) (Venter et al 2020 ; Xing et al 2021 ; Bonardi et al 2021 ) and most countries and regions have a lot of lock-down days in this period (Venter et al 2020 ): Pakistan (Mehmood et al 2021a ; Mehmood et al 2021b ; Khan 2021 ; Aslam et al 2021 ), Afghanistan and India (Mishra and Kulshrestha 2021 ; Gautam et al 2021 ), Turkmenistan (Zhang 2021 ), Azerbaijan (Bonardi et al 2021 ), Armenia (Bonardi et al 2021 ), Turkey (Ghasempour et al 2021 ; Dursun et al 2022 ), Iraq (Hashim et al 2021 ; Hashim et al 2021 ), Kazakhstan (Kerimray et al 2020 ), Bahrain (Benchrif et al 2021 ; Qaid et al 2022 ), Kuwait (Halos et al 2021 ), Oman (Bonardi et al 2021 ), Qatar (Mahmoud et al 2022 ), Saudi Arabia (Ghanim 2021 ; Habeebullah et al 2022 ; Anil and Alagha 2021 ; Morsy et al 2021 ), UAE (Alqasemi et al 2021 ; Teixidó et al 2021 ; Alalawi et al 2022 ; Shanableh et al 2022 ), Asia (Baniasad et al 2021 ) and Iran (Moazeni 2021 ; Broomandi et al 2020 ; Keshtkar 2022 ; Norouzi and Asadi 2022 ).…”

Section: Introductionmentioning

confidence: 99%

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

Fayaz

2022

Model. Earth Syst. Environ.

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Introduction The COVID-19 restrictions have a lot of various peripheral negative and positive effects, like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world. Methods Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air pollution indices of them such as CH 4 (Methane), CO_1 (CO), H 2 O (Water), HCHO (Tropospheric Atmospheric Formaldehyde), NO 2 (Nitrogen oxides), O 3 (ozone), SO 2 (Sulfur Dioxide), UVAI_AAI [UV Aerosol Index (UVAI)/Absorbing Aerosol Index (AAI)] are studied from the First quarter of 2019 to the fourth quarter of 2021 with Copernicus Sentinel 5 Precursor (S5P) satellite data set from Google Earth Engine. The outliers are detected based on the depth functions. We use a two-sample t test, Wilcoxon test, and interval-wise testing for functional data to control the familywise error rate. Result The adjusted p value comparison between Q2 of 2019 and Q2 of 2020 in NO 2 for almost all countries is statistically significant except Iraq, UAE, Bahrain, Qatar, and Kuwait. But, the CO and HCHO are not statistically significant in any country. Although CH 4 , O 3 , and UVAI_AAI are statistically significant for some countries. In the Q2 comparison for NO 2 between 2020 and 2021, only Iran, Armenia, Turkey, UAE, and Saudi Arabia are statistically significant. However, Ch 4 is statistically significant for all countries except Azerbaijan. Conclusions The comparison with and without adjusted p values declares the decreases in some air pollution in these countries. Supplementary Information The online version contains supplementary material available at 10.1007/s40808-022-01528-x.

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Section: Introductionmentioning

confidence: 99%

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

Fayaz

2022

Model. Earth Syst. Environ.

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“…Air quality improvement around the world can be described as an unprecedented air quality experiment. Several studies conducted around the world support this argument that air pollutants' reduction in the atmosphere has been observed as a result of national lockdowns con rming that national lockdowns led to air pollutants' reduction in the atmosphere (Health Effects Institute 2020; IQAir 2020; Pozzer et al 2020; Rodríguez-Urrego and Rodríguez-Urrego 2020) (Moazeni et al 2022). Despite the positive effect of the global lockdown on air quality, the period of isolation has been viewed as an economic crisis across the globe.…”

Section: Ozone (O 3 )mentioning

confidence: 94%

Progress, Challenges and Prospects of Urban Air Pollution and Climate in Iran

Taghizadeh

Mokhtarani

Rahmanian

2022

Preprint

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Air pollution has been integrated into global challenges over the last few years due to its negative impact on the health of human beings, increasing socio-economic risks and its contribution to climate change. This paper highlights the general status of air pollution worldwide. It then attempts to systematically evaluate the current status of Iran's air pollution with regard to the sources of emissions, control policies, as well as the health and climate consequences that have resulted through available data from monitoring stations reported in the literature and official documents. The challenges and opportunities are also discussed to provide feedback on the efficacy of control measures and policy-making. Many large cities in Iran surpass the permissible concentration of air pollutants, particularly particulate matter, sulfur dioxide, black carbon and ozone. Although the regulations and policies are in place and enormous efforts are being made to address air pollution issues in the country, implementation and enforcement are not as effective as they could be. The significant challenges may be regarded as the inefficiency of regulation and supervision systems, the lack of air quality monitoring systems and technology, particularly in industrial cities rather than Tehran as well as the lack of continual feedback and investigations on the efficiency of regulation. Providing such a comprehensive and up-to-date report can bring opportunities for international collaboration, which is essential in addressing the air pollution issue worldwide.

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“…Ordinary Kriging is one of the most widely used spatial interpolation techniques that leverages the spatial autocorrelation structure of observed locations to estimate values at unmeasured locations [64]. [33] applied ordinary Kriging with a spherical semi-variogram model based on observations of the children's elevated blood lead level (BLL) geocoded to the home address to visualize BLL variations before and after water source changes.…”

Section: Spatial Interpolationmentioning

confidence: 99%

Characterizing Patient Phenotypes and Emerging Trends in Application of Spatial Analysis in Individual-Level Health Data

Mollalo,

Hamidi,

Lenert

et al. 2023

Preprint

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Objectives: Health information systems (HIS) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients in biomedical studies. Despite the widespread adoption of HIS, no systematic review has examined the extent to which spatial analysis is used in characterizing patient phenotypes. Materials and Methods: We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, geography, or specific health domains. Results: Only 62 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. Geographically, the use was limited, involving only nine countries, with over 80% of studies conducted in the United States. Moreover, a noteworthy surge (82.3%) in publications was observed post-2017. The publications investigated various clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visit. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized. Discussion and Conclusion: This review underscores the growing interest in spatial analysis of HIS-derived data and highlights knowledge gaps in clinical health, phenotype domains, geospatial distribution, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future biomedical research.

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Spatiotemporal analysis of COVID-19, air pollution, climate, and meteorological conditions in a metropolitan region of Iran

Cited by 14 publications

References 67 publications

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors

Progress, Challenges and Prospects of Urban Air Pollution and Climate in Iran

Characterizing Patient Phenotypes and Emerging Trends in Application of Spatial Analysis in Individual-Level Health Data

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