Connection between road density and landscape fragmentation in Hungary using kernel density based on gis methods

Humans have recently caused significant landscape fragmentation by developing transportation infrastructure. We used Kernel density estimation (KDE) to analyze the road density distribution in Hungary, and then we assessed landscape fragmentation after imposing the road density onto the land-use map of Hungary, using Mean Patch Area, Patch Density, and Number of Patches as three important landscape metrics. Our analysis shows that roads, as expected, are mainly located in artificial lands (58.15%) and farmland (28.16%) landscapes. PD and NP increased by 69.59% and 69.51%, respectively, at the landscape scale, while AREA MN decreased by 41%. It has been proved by Spearman's rank correlation coefficient analysis which showed that the road density showed a positive correlation with PD and NP and a negative correlation with AREA_MN. This means that the higher the road density, the higher the PD and NP values, and the smaller the patch area. Furthermore, landscape fragmentation is positively related to road density, and as the road system became denser, the landscape became more fragmented. Understanding the effects of road networks on various land uses can aid in the development of sustainable road systems in Hungary

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

Atmospheric particulate matter (PM10) is one of the most important pollutants for human health, and road transport could be a major anthropogenic source of it. Several research studies have shown the impact of roads on the air quality in urban areas, but the relationship between road and rail networks and ambient PM10 concentrations has not been well studied, especially in suburban and rural landscapes. In this study, we examined the link between the spatial characteristics of each road type (motorway, primary road, secondary road, and railway) and the annual average PM10 concentration. We used the European 2931 air quality (AQ) station dataset, which is classified into urban, suburban, and rural landscapes. Our results show that in urban and rural landscapes, the spatial characteristics (the density of the road network and its distance from the AQ monitoring points) have a significant statistical relationship with PM10 concentrations. According to our findings from AQ monitoring sites within the urban landscape, there is a significant negative relationship between the annual average PM10 concentration and the density of the railway network. This result can be explained by the driving wind generated by railway trains (mainly electric trains). Among the road network types, all road types in the urban landscape, only motorways in the suburban landscape, and only residential roads in the rural landscape have a significant positive statistical relationship with the PM10 values at the AQ monitoring points. Our results show that in the suburban zones, which represent the rural–urban fringe, motorways have a strong influence on PM-related air pollution. In the suburban areas, the speed of vehicles changes frequently near motorways and intersections, so higher traffic-related PM10 emission levels can be expected in these areas. The findings of this study can be used to decrease transportation-related environmental conflicts related to the air quality in urban, urban–rural fringe, and rural (agricultural) landscapes

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

Atmospheric particulate matter (PM10) is one of the most important pollutants for human health, and road transport could be a major anthropogenic source of it. Several research studies have shown the impact of roads on the air quality in urban areas, but the relationship between road and rail networks and ambient PM10 concentrations has not been well studied, especially in suburban and rural landscapes. In this study, we examined the link between the spatial characteristics of each road type (motorway, primary road, secondary road, and railway) and the annual average PM10 concentration. We used the European 2931 air quality (AQ) station dataset, which is classified into urban, suburban, and rural landscapes. Our results show that in urban and rural landscapes, the spatial characteristics (the density of the road network and its distance from the AQ monitoring points) have a significant statistical relationship with PM10 concentrations. According to our findings from AQ monitoring sites within the urban landscape, there is a significant negative relationship between the annual average PM10 concentration and the density of the railway network. This result can be explained by the driving wind generated by railway trains (mainly electric trains). Among the road network types, all road types in the urban landscape, only motorways in the suburban landscape, and only residential roads in the rural landscape have a significant positive statistical relationship with the PM10 values at the AQ monitoring points. Our results show that in the suburban zones, which represent the rural–urban fringe, motorways have a strong influence on PM-related air pollution. In the suburban areas, the speed of vehicles changes frequently near motorways and intersections, so higher traffic-related PM10 emission levels can be expected in these areas. The findings of this study can be used to decrease transportation-related environmental conflicts related to the air quality in urban, urban–rural fringe, and rural (agricultural) landscapes