Effect of struvite (Crystal Green) fertilization on soil element content determined by different methods under soybean cultivation.

Struvite is regarded as a promising phosphorus fertilizer alternative to mineral fertilizers; however before fertilizing, soil tests should be undertaken to determine fertilizer recommendations. In May 2022, soil was sampled from a pot experiment with the application of phosphorus set up at the Wroclaw University and Environmental and Life Sciences. Chemical analysis of the soil included total and available phosphorus, potassium, magnesium determined by the Egner-Riehm, Mehlich 3 and Yanai methods. The purpose of the article is to compare soil element extraction by three different methods under struvite fertilization and its use in soybean cultivation. The application of these methods indicated an unambiguous increase in soil Mg content after struvite application. Broadcast soybean fertilization affected the phosphorus content of the soil. The results of the study indicated that different extraction methods presented different contents of P from soil. The content of available phosphorus was circa 122-156 mg kg-1 dm, 35.4-67.5 mg kg-1 dm and 100-159 mg kg-1 dm according to the Mehlich, Yanai and Egner-Riehm methods, respectively. A positive correlation was found between the content of Mg and K in soil determined by the Mehlich 3 and Yanai methods, which may suggest that the Yanai method could be introduced into standard soil chemical analysis in Poland. Such a correlation was not found for phosphorus, which is a difficult element to determine due to the multitude of factors affecting its availability.

Comparison of active and passive methods for atmospheric particulate matter collection: From case study to a useful biomonitoring tool.

In this study we conducted air pollution monitoring using three different methods: active monitoring with the use of high volume aerosol sampler and biomonitoring with the use of lichens and spider webs. All of these monitoring tools were exposed to air pollution in Legnica city, a region of Cu-smelting in the SW Poland, which is well known for exceeding the environmental guidelines. Quantitative analysis was carried out for the particles collected by the three selected methods and concentrations of seven selected elements (Zn, Pb, Cu, Cd, Ni, As, Fe) were obtained. Concentrations found in lichens and in spider webs were directly compared and indicated significant differences between them, with higher amounts noted for spider webs. Then, in order to recognize the main pollution sources the principal component analysis was conducted and obtained results were compared. It resulted that spider webs and aerosol sampler, despite different mechanisms of accumulation, show similar sources of pollution - in this case - copper smelter. Additionally, the HYSPLIT trajectories and the correlations between metals in the aerosol samples also confirmed that this is the most probable source of pollution. This study can be considered innovative as these three air pollution monitoring methods were compared, which has never been conducted before, and their comparison gave satisfying results.

Whether cycling around the city is in fact healthy in the light of air quality - Results of black carbon.

Studying the air quality and exposure of the inhabitants of urban agglomerations to pollution is the basis for the creation and development of more sustainable cities. Although research on black carbon (BC) has not yet reached the official acceptable levels and guidelines, the World Health Organization clearly indicates the need to measure and control the level of this pollutant. In Poland, monitoring of the level of BC concentration is not included in the air quality monitoring network. To estimate the extent of this pollutant to which pedestrians and cyclists are exposed, mobile measurements were carried out on over 26 km of bicycle paths in Wroclaw. The obtained results indicate the influence of urban greenery next to the bicycle path (especially if the cyclist is separated from the street lane by hedges or other tall plants) and the 'breathability' (i.e., associated with surrounding infrastructure) of the area on the obtained concentrations; the average concentration of BC in such places ranged from 1.3 to 2.2 µg/m3, whereas a cyclist riding directly on bike paths adjacent to the main roads in the city center is exposed to concentrations in the range of 2.3-14 µg/m3. The results of the measurements, also related to stationary measurements made at a selected point of one of the routes, clearly indicate the importance of the infrastructure surrounding the bicycle paths, their location, and the impact of urban traffic on the obtained BC concentrations. The results presented in our study are based only on short-term-field campaigns preliminary studies. To determine the quantitative impact of the characteristics of the bicycle route on the concentration of pollutants, and thus the exposure of users, the systematized research should cover a greater part of the city and be representative in terms of various hours of the day.

The importance of data splitting in combined NOx concentration modelling.

The polluted air breathed every day by those living in large conurbations poses a significant risk to their health. Through effective modelling (prediction) of concentrations of pollutants and identification of the factors influencing them, it should be possible to obtain advance information on dangers and to plan and implement measures to reduce them. This work describes two different modelling approaches: based on the NOx concentration of the previous hour (C&RT models); and based on meteorological factors, traffic flow, and past (up to two previous hours) NOx and NO2 concentrations (CA models). For each approach, three alternative machine learning methods were applied: artificial neutral network (ANN), random forest (RF), and support vector regression (SVR). The best fits were obtained for the models using ANN and RF (MAPE values in the range 18.3-18.5 %). Poorer fits were found for the SVR models (MAPE equal to 23.4 % for the C&RT approach and 29.3 % for CA). No significant preferences were identified between the C&RT and CA approaches (based on various goodness-of-fit measures). The choice should be determined by the purposes for which the forecast is to be used.

A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions.

High concentrations of nitrogen dioxide in the air, particularly in heavily urbanised areas, have an adverse effect on many aspects of residents' health (short-term and long-term damage, unpleasant odour and other). A method is proposed for modelling atmospheric NO2 concentrations in a conurbation, using a partition model M consisting of two separate models: ML for lower concentration values and MU for upper values. An advanced data mining technique, that of random forests, is used. This is a method based on machine learning, involving the simultaneous compilation of information from multiple random trees. Using the example of data recorded in Wroclaw (Poland) in 2015-2017, an iterative method was applied to determine the boundary concentration y˜ for which the mean absolute deviation error for the partition model attained its lowest value. The resulting model had an R2 value of 0.82, compared with 0.60 for a classical random forest model. The importances of the variables in the model ML, similarly as in the classical case, indicate that the greatest influence on NO2 concentrations comes from traffic flow, followed by meteorological factors, in particular the wind direction and speed. In the model MU the importances of the variables are significantly different: while traffic flow still has the greatest impact, the effects of temperature and relative humidity are almost as great. This confirms the justifiability of constructing separate models for low and high pollution concentrations.

The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: A case study in Wroclaw.

Random forests, an advanced data mining method, are used here to model the regression relationships between concentrations of the pollutants NO2, NOx and PM2.5, and nine variables describing meteorological conditions, temporal conditions and traffic flow. The study was based on hourly values of wind speed, wind direction, temperature, air pressure and relative humidity, temporal variables, and finally traffic flow, in the two years 2015 and 2016. An air quality measurement station was selected on a main road, located a short distance (40 m) from a large intersection equipped with a traffic flow measurement system. Nine different time subsets were defined, based among other things on the climatic conditions in Wroclaw. An analysis was made of the fit of models created for those subsets, and of the importance of the predictors. Both the fit and the importance of particular predictors were found to be dependent on season. The best fit was obtained for models created for the six-month warm season (April-September) and for the summer season (June-August). The most important explanatory variable in the models of concentrations of nitrogen oxides was traffic flow, while in the case of PM2.5 the most important were meteorological conditions, in particular temperature, wind speed and wind direction. Temporal variables (except for month in the case of PM2.5) were found to have no significant effect on the concentrations of the studied pollutants.