Spatial Association of Urban Form and Particulate Matter

Abstract

Increasingly detrimental effects of fine particulate matter (PM) have been observed in Northeast Asia owing to its rapid economic development. Previous studies have found that dust, combustion, and chemical reactions are the major sources of PM; nevertheless, the spatial configuration of land use and land cover, which is of most interest to planners and landscape architects, also influences the PM levels. Here, we attempted to unveil the relationship between PM and different types of land use cover (i.e., developed, agricultural, woody, grass, and barren lands) in 122 municipalities of Korea. Landscape ecology metrics were applied to measure the spatial configuration of land use pattern and spatial lag models by taking into account the transboundary nature of air pollution, allowing us to conclude the following regarding PM levels: (1) the size of land cover type matters, but their spatial configuration also determines the variations in PM levels; (2) the contiguity and proximity of landcover patches are important; (3) the patterns of grasslands (e.g., simple, compact, and cluster (with large patches) patterns) and woodlands (e.g., complex, contiguous, and cluster (with large patches) patterns) considered desirable for minimizing PM are dissimilar in terms of contiguity.

Relationships between Thermal Environment and Air Pollution of Seoul’s 25 Districts Using Vector Autoregressive Granger Causality

“…Lastly, this holistic approach to managing the thermal environment and air pollution should be guided by the region-wide real-time monitoring Our findings are generally in line with what previous studies set forth. The one-way causal relationships, where either thermal environment is the independent variable and air pollution is the dependent [21][22][23][24][25][26][27][28][29][30] or the other way around [31][32][33][34], are evident in all districts of Seoul. At the same time, each district yields at least one two-way causal relationship, as another body of literature suggests [35][36][37][38][39][40].…”

“…Notably, a larger body of literature finds that temperatures and concentrations of PM 10 operate as a cause for each other in cities using statistical approaches in which usually independent and dependent variables are fixed. They identify temperature as a statistically significant or highly important explanatory variable when predicting PM 10 concentrations [21][22][23][24][25][26][27], as heat increases energy use, thereby emitting more air pollutants and generating secondary aerosols in the atmosphere [28][29][30]. Another substantial body presents evidence that particulates induce discernible temperature fluctuations, often facilitated through radiative forcing mechanisms [31][32][33][34].…”

Prediction of CO pollutant in Mashhad metropolis, Iran: Using multiple linear regression

“…The largest human sources of CO production in the United States are vehicle emissions (National Academies Press, 2021) and combustion. CO pollutant is also one of the sources of fine particles (PM 2.5 ) (Park et al, 2021). In the Los Angeles area, a large correlation was found between CO and particulate emissions ( R 2 = 0.65).…”