Seyedehmehrmanzar Sohrab

Bulk density (BD) is among the most important physical soil properties, influencing many other soil properties, functions and services. An earlier study revealed that BD observations in the Hungarian Soil Information and Monitoring System (SIMS) contain errors and need correction. The objective of this research was to correct BD measurements in SIMS using advanced pedotransfer functions (PTFs) (i.e., multiple linear regression, generalized additive model, cubist, random forest, and artificial neural networks) developed based on the Hungarian Detailed Soil Hydrophysical Database and environmental covariates serving as proxies for the soil forming factors. The developed PTFs were evaluated and compared using cross‐validation. It was found that random forest (RF) outperformed other techniques, with RMSE and model efficiency coefficient values of 0.099 g cm ⁻³ and 0.539, respectively. An RF‐based PTF was used to correct BD measurements in SIMS and to provide quantitative information on the uncertainty associated with the corrected BD values. The corrected dataset consists of information on the profile and layer ID of the SIMS monitoring sites, the upper and lower depth boundaries of each soil genetic horizon, the corrected BD values, as well as the uncertainty associated with them. The dataset and the developed codes are freely available on Zenodo and GitHub, respectively. The use of the corrected BD dataset is recommended not only for soil scientists but also for researchers from various disciplines. The shared dataset can be of interest not only for Hungarian applications but also for continental and global initiatives aimed at soil health monitoring, land degradation neutrality, and sustainable agriculture.

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM₁₀). Understanding the spatiotemporal variations of PM₁₀ is essential for developing effective control strategies. This study aimed to enhance PM₁₀ prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM₁₀ concentrations, and analyzing their correlations with PM₁₀ air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM₁₀ air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM₁₀ prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM₁₀ concentration. The SHDI was negatively correlated with PM₁₀ levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM₁₀ predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM₁₀ sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM₁₀ pollution.

Background: PM₁₀, comprising particles with diameters of 10 µm or less, has been identified as a significant environmental pollutant associated with adverse health outcomes in European cities. Understanding the temporal variation of the relationship between PM₁₀ and geographical parameters is crucial for sustainable land use planning and air quality management in European landscapes. This study utilizes Conditional Inference Forest modeling and partial correlation to examine the impact of geographical factors on monthly average concentrations of PM₁₀ in European suburban and urban landscapes during heating and cooling periods. The investigation focuses on two buffer zones (1000 m and 3000 m circle radiuses) surrounding 1216 European air quality monitoring stations. Results: Results reveal importance and significant correlations between various geographical variables (soil texture, land use, transportation network, and meteorological) and PM₁₀ quality on a continental scale. In suburban landscapes, soil texture, temperature, roads, and rail density play pivotal roles, while meteorological variables, particularly monthly average temperature and wind speed, dominate in urban landscapes. Urban sites exhibit higher R-squared values during both cooling (0.41) and heating periods (0.61) compared to suburban sites (cooling period R-squared: 0.39; heating period: R-squared: 0.51), indicating better predictive performance likely attributed to the less heterogeneous land use patterns surrounding urban PM₁₀ monitoring sites. Conclusion: The study underscores the importance of investigating spatial and temporal dynamics of geographical factors for accurate PM₁₀ air quality prediction models in European urban and suburban landscapes. These findings provide valuable insights for policymakers, urban planners, and environmental scientists, guiding efforts toward sustainable and healthier urban environments.

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.