Spatial and Temporal Variations in Atmospheric Ventilation Index Coupled with Particulate Matter Concentration in South Korea

Abstract

The spatiotemporal variations in the atmospheric ventilation index (AVI) with the particulate matter (PM) concentrations in South Korea were investigated using a regional grid model derived from the National Center for AgroMeteorology and PM10 concentration data obtained from AirKorea and the Korea Meteorological Administration. To construct a high-resolution AVI database with 1 h time intervals and a spatial resolution of approximately 2.4 km, a medium-range prediction was performed using a regional model twice a week from December 2018 to November 2019. The resultant dataset was used to explore the seasonal patterns of the areal distribution of a novel index: Ventilation Index coupled with PM (VIP), defined by the ratio of the AVI to PM. To determine the effects of geography on the VIP, diurnal variations of the VIP were examined at three major cities in South Korea. The emphasis of the investigation was on major cities that are planned to be developed into smart cities. This study reveals the specific spatiotemporal structure of the AVI in South Korea for the first time at a high resolution and introduced the potential usefulness of the VIP. The results provide insights that could aid decision making for determining favorable locations for clean or polluted cities on an annual basis and can enable the sustainable management of fine PM in and around the areas of interest.

Connection between the Spatial Characteristics of the Road and Railway Networks and the Air Pollution (PM10) in Urban–Rural Fringe Zones

“…The fact that the effect of the distance from roads on PM10 pollution could not be detected at the AQ stations where the source of pollution (industry or traffic) is precisely known suggests that other factors, such as land use, climate [61,65], topography, and soil characteristics, have a greater influence on PM10 concentration levels. This implies that future studies investigating the causes of PM10 pollution will require the inclusion of multiple datasets, such as land use type, annual wind speed, soil type, and topography datasets, and the use of multivariate models as a method of analysis may provide new insights into the problem [2,19,[66][67][68][69].…”