Correlation Between Relative Humidity and Particulate Matter During the Ongoing of Pandemic: A Systematic Review

Abstract

Particulate matter (PM) has been demonstrated to be hazardous to the human body. Various studies have identified the source of PM, but the aggravating factors have not been thoroughly clarified. As a result, preventing or intervening in this problem is critical. The goal of this study is to assess the overall strength of the evidence for the relationship between relative humidity (RH) and PM to create a plan or guideline using water or humidity technique for dealing with and preventing future PM problems. A comprehensive search of articles published in English was conducted across three electronic databases (PubMed, Scopus, and SpringerLink) in January 2023, using articles available from the inception of the first cluster of COVID-19, on December 1, 2019, until January 12, 2023. Articles were screened against inclusion/exclusion criteria and data from included studies were retrieved and analyzed. Of the 3799 records found, only 52 met the initial inclusion and only 27 articles were included in the final qualitative synthesis. Around forty percent of the studies exhibited the correlation between coarse particulate matter and relative humidity. Half of the total studies found a direct correlation between PM_2.5 and RH, while PM₁₀ and RH have the opposite correlation. Most of the studies demonstrated that the correlation between relative humidity and particulate matter is significant. This study suggests that spraying water or increasing humidity to reduce air pollution may decrease the larger-sized dust particles, but have the opposite effect on smaller-sized particles. Those reviewed studies briefly explained the mechanism behind their results, thus providing insight for further investigation and assisting policymakers in staying on track while producing working models. Both simulations and multivariate studies should be conducted as part of these further investigations. For future research, the use of artificial intelligence (AI) or machine learning model and a meta-analysis between PM chemical components and RH are recommended.