Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

Abstract

It remains unclear on how PM2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM10, SO2 and NO2 is synchronous with that of PM2.5. At both daily and monthly scales, PM2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM2.5 was positively correlated with O3, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

“…Thereafter, based on the defined time series, the six parameters of NO 2 , SO 2 , CO, HCHO, O 3 , and AI from Sentinel-5P, and the DEM product from SRTM were extracted and averaged for the model training. Regarding Sentinel-5P data, we selected CO and HCHO due to their similar emission source to that of PM 2.5 [48][49][50][51], SO 2 and NO 2 as precursors for the PM 2.5 [51][52][53], and O 3 due to its varying interaction with PM 2.5 by seasons and regions [54][55][56]. Lastly, we also included the product of AI as an informative parameter about PM 2.5 [57], since it captures signals of aerosols emitted from biomass burning or fire events [58], which is another source of particulate matter [59,60].…”

A cohort study evaluating the risk of stroke associated with long-term exposure to ambient fine particulate matter in Taiwan

“…It is worth noting that the correlation between PM 2.5 concentrations and temperature varies by several factors such as time scales, geographic regions and components of PM 2.5 . Fu et al reported the correlation between PM 2.5 levels and temperature was positive at the daily scale, but was negative at the monthly scale in China [ 47 ]. A multiple linear regression model used to analyze 11-year records in the US revealed positive relations between ambient PM 2.5 concentrations and temperature with varied strengths across regions [ 48 ].…”

Airborne bacterial community associated with fine particulate matter (PM2.5) under different air quality indices in Temuco city, southern Chile

“…Moreover, airborne culturable bacteria community was also found to be higher and positively correlated with particulate matter concentration and chemical composition in Asian dust events (Jeon et al 2011). Chemical components of aerosols, such as CO and CO 2 , have shown a higher positive correlation with the bacterial community, explaining approximately 55% of the variance (Innocente et al 2017) associated with firewood combustion (Fu et al 2020;Xu et al 2017;Zhong et al 2019;Li et al 2019). Our findings show that despite the high pollution and carbon concentration in particulate matter produced in Temuco during colder days and due to residential firewood combustion, the bacterial community can be detected, and its diversity increases with air quality deterioration.…”

“…Moreover, PM 10 and CO also exhibited strong positive correlations between them (R = 0.835) and strong negative with T (R = − 0.788, − 0.754). Fu et al (2020) showed that PM 2.5 is positively correlated with CO during polluted days in China. It is also commonly observed that the spatial distribution of CO and particulate matter concentration tends to be the same in most parts of the day (Wang et al 2017;Chuai and Feng 2019).…”