Effect of Meteorological Conditions and Anthropogenic Factors on Air Concentrations of PM2.5 and PM10 Particulates on the Examples of the City of Kielce, Poland

The paper analyzes the influence of meteorological conditions (air temperature, wind speed, humidity, visibility) and anthropogenic factors (population in cities and in rural areas, road length, number of vehicles, emission of dusts and gases, coal consumption in industrial plants, number of air purification devices installed in industrial plants) on the concentration of PM2.5 and PM10 dusts in the air in the region of Kielce city in Poland. Spearman correlation coefficient was used to evaluate the relationship between the mentioned independent variables and air quality indicators. The calculated values of the correlation coefficient showed statistically significant relationships between air quality and the amount of installed air purification equipment in industrial plants. A statistically significant effect of the population in rural settlement units on the increase in air concentrations of PM2.5 and PM10 was also found, which proves the influence of the so-called low emission of pollutants on the air quality in the studied region. The analyses also revealed a statistically significant effect of road length on the decrease in PM2.5 and PM10 air content. This result indicates that a decrease in traffic intensity on particular road sections leads to an improvement in air quality. The analyses showed that despite the progressing anthropopression in the Kielce city region the air quality with respect to PM2.5 and PM10 content is improving. To verify the results obtained from statistical calculations, parametric models were also determined to predict PM2.5 and PM10 concentrations in the air, using the methods of Random Forests (RF), Boosted Trees (BT) and Support Vector Machines (SVM) for comparison purposes. The modelling results confirmed the conclusions that had been made based on previous statistical calculations

Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach

Abstract. One of the key parameters constituting the basis for the operational assessment of stormwater systems is the annual number of storm overflows. Since uncontrolled overflows are a source of pollution washed away from the surface of the catchment area, which leads to imbalanced receiving waters, there is a need for their prognosis and potential reduction. The paper presents a probabilistic model for simulating the annual number of storm overflows. In this model, an innovative solution is to use the logistic regression method to analyze the impact of rainfall genesis on the functioning of a storm overflow (OV) in the example of a catchment located in the city of Kielce (central Poland). The developed model consists of two independent elements. The first element of the model is a synthetic precipitation generator, in which the simulation of rainfall takes into account its genesis resulting from various processes and phenomena occurring in the troposphere. This approach makes it possible to account for the stochastic nature of rainfall in relation to the annual number of events. The second element is the model of logistic regression, which can be used to model the storm overflow resulting from the occurrence of a single rainfall event. The paper confirmed that storm overflow can be modeled based on data on the total rainfall and its duration. An alternative approach was also proposed, providing the possibility of predicting storm overflow only based on the average rainfall intensity. Substantial simplification in the simulation of the phenomenon under study was achieved compared with the works published in this area to date. It is worth noting that the coefficients determined in the logit models have a physical interpretation, and the universal character of these models facilitates their easy adaptation to other examined catchment areas. The calculations made in the paper using the example of the examined catchment allowed for an assessment of the influence of rainfall characteristics (depth, intensity, and duration) of different genesis on the probability of storm overflow. Based on the obtained results, the range of the variability of the average rainfall intensity, which determines the storm overflow, and the annual number of overflows resulting from the occurrence of rain of different genesis were defined. The results are suited for the implementation in the assessment of storm overflows only based on the genetic type of rainfall. The results may be used to develop warning systems in which information about the predicted rainfall genesis is an element of the assessment of the rainwater system and its facilities. This approach is an original solution that has not yet been considered by other researchers. On the other hand, it represents an important simplification and an opportunity to reduce the amount of data to be measured

EVALUATION OF MODERN HERITAGE ASSETS IN POZNAN USING MONOTONIC DECISION RULES

The protection of cultural heritage is an important task of communities on various levels of social organization. The institutionalization of the processes of protection of modern heritage assets provides the necessary instruments (legislative, juridical, financial) enabling the actual realization of the assumed tasks. The criterion of age, which is still a dominating premise for monument protection, proved not to be sufficient, especially concerning protection of monuments of Modernism. A step that led to the determination of the value of individual architectural objects of the 20th century was the establishment of 10 evaluation criteria proposed by historians of architecture in Warsaw, and afterwards in Poznan.In this work, we focus on the architectural value of post-war buildings, which are most difficult to evaluate. Furthermore, we wanted to apply AI to objectify the process of decision making. The adequacy of the Dominance-based Rough Set Approach (DRSA) method has been established. This method takes into account preference orders on criteria and models patterns observed in data in terms of monotonic “if &hellip;, then &hellip;” decision rules.</p

An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume

An innovative tool for modeling the specific flood volume was presented that can be applied to assess the need for stormwater network modernization as well as for advanced flood risk assessment. Field measurements for a catchment area in Kielce, Poland, were used to apply the model and demonstrate its usefulness. This model extends the capability of recently developed statistical and machine learning hydrodynamic models developed from multiple runs of the US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The extensions enable the inclusion of (1) the characteristics of the catchment and its stormwater network, calibrated model parameters expressing catchment retention, and the capacity of the sewer system; (2) extended sensitivity analysis; and (3) risk analysis. Sensitivity coefficients of calibrated model parameters include correction coefficients for percentage area, flow path, depth of storage, and impervious area; Manning roughness coefficients for impervious areas; and Manning roughness coefficients for sewer channels. Sensitivity coefficients were determined with respect to rainfall intensity and characteristics of the catchment and stormwater network. Extended sensitivity analysis enabled an evaluation of the variability in the specific flood volume and sensitivity coefficients within a catchment, in order to identify the most vulnerable areas threatened by flooding. Thus, the model can be used to identify areas particularly susceptible to stormwater network failure and the sections of the network where corrective action should be taken to reduce the probability of system failure. The simulator developed to determine the specific flood volume represents an alternative approach to the SWMM that, unlike current approaches, can be calibrated with limited topological data availability; therefore, the aforementioned simulator incurs a lower cost due to the lower number and lower specificity of data required.</p

The Effect of Lacticaseibacillus paracasei LPC100 and Lactiplantibacillus plantarum LP140 on Bone Mineral Density in Postmenopausal Women: A Multicenter, Randomized, Placebo-Controlled Study

Objectives: modulation of gut microbiota by probiotics has been proposed as a target for intervention to reduce bone mineral density (BMD) loss in the postmenopausal period. This study aims to evaluate the effect of Lacticaseibacillus (L.) paracasei LPC100 and Lactiplantibacillus (L.) plantarum LP140 on BMD in postmenopausal women in a multicenter, randomized, double-blind, placebo-controlled trial. Methods: the primary outcome was the change in T-score of the lumbar spine measured by dual-energy X-ray absorptiometry assessed after twelve-month probiotic supplementation. Secondary outcomes included changes in serological markers of inflammation and calcium–phosphate metabolism, body mass index, gastrointestinal symptoms, and satisfaction with the intervention. Results: a decrease in T-score indicating the progressive bone demineralization reached a statistically significant difference in the placebo group (from mean values of 0.06 ± 1.04 to −0.28 ± 1.12, p = 0.041) but not in the probiotic group (0.19 ± 0.99 and −0.08 ± 1.05, respectively, p = 0.125) with a p-value = 0.089 between the groups. No significant differences were found in secondary outcomes with the exception of vitamin D concentration and a significant reduction in some gastrointestinal symptoms in the probiotic group. A significant decrease in vitamin D level was observed only in the placebo group (p = 0.014). Probiotics were safe and well tolerated. Conclusions: this study suggests that the oral administration of L. paracasei LPC100 and L. plantarum LP140 may be a viable strategy to prevent BMD loss and vitamin D deficiency in postmenopausal women, but further research is necessary to confirm clinical benefits and to know the mechanism of action [ClinicalTrial.gov NCT06375668]

Heppa III Intercomparison Experiment on Electron Precipitation Impacts: 2. Model‐Measurement Intercomparison of Nitric Oxide (NO) During a Geomagnetic Storm in April 2010

Precipitating auroral and radiation belt electrons are considered to play an important part in the natural forcing of the middle atmosphere with a possible impact on the climate system. Recent studies suggest that this forcing is underestimated in current chemistry-climate models. The HEPPA III intercomparison experiment is a collective effort to address this point. In this study, we apply electron ionization rates from three data-sets in four chemistry-climate models during a geomagnetically active period in April 2010. Results are evaluated by comparison with observations of nitric oxide (NO) in the mesosphere and lower thermosphere. Differences between the ionization rate data-sets have been assessed in a companion study. In the lower thermosphere, NO densities differ by up to one order of magnitude between models using the same ionization rate data-sets due to differences in the treatment of NO formation, model climatology, and model top height. However, a good agreement in the spatial and temporal variability of NO with observations lends confidence that the electron ionization is represented well above 80 km. In the mesosphere, the averages of model results from all chemistry-climate models differ consistently with the differences in the ionization-rate data-sets, but are within the spread of the observations, so no clear assessment on their comparative validity can be provided. However, observed enhanced amounts of NO in the mid-mesosphere below 70 km suggest a relevant contribution of the high-energy tail of the electron distribution to the hemispheric NO budget during and after the geomagnetic storm on April 6

New records and noteworthy data of plants, algae and fungi in SE Europe and adjacent regions, 13

This paper presents new records and noteworthy data on the following taxa in SE Europe and adjacent regions: brown alga Heribaudiella fluviatilis, red alga Batrachospermum skujae, saprotrophic fungus Gnomonia geranii-macrorrhizi, mycorrhizal fungi Amanita alseides and Russula griseascens, liverwort Ricciocarpos natans, moss Blindia acuta, Leucodon sciuroides var. morensis and Pseudostereodon procerrimus, monocots Allium ampeloprasum, Carex ferruginea and Carex limosa and dicots Convolvulus althaeoides, Fumana aciphylla, Hieracium petrovae, Lamium bifidum subsp. bifidum and Ranunculus fontanus are given within SE Europe and adjacent region

Data Descriptor : A European Multi Lake Survey dataset of environmental variables, phytoplankton pigments and cyanotoxins

Under ongoing climate change and increasing anthropogenic activity, which continuously challenge ecosystem resilience, an in-depth understanding of ecological processes is urgently needed. Lakes, as providers of numerous ecosystem services, face multiple stressors that threaten their functioning. Harmful cyanobacterial blooms are a persistent problem resulting from nutrient pollution and climate-change induced stressors, like poor transparency, increased water temperature and enhanced stratification. Consistency in data collection and analysis methods is necessary to achieve fully comparable datasets and for statistical validity, avoiding issues linked to disparate data sources. The European Multi Lake Survey (EMLS) in summer 2015 was an initiative among scientists from 27 countries to collect and analyse lake physical, chemical and biological variables in a fully standardized manner. This database includes in-situ lake variables along with nutrient, pigment and cyanotoxin data of 369 lakes in Europe, which were centrally analysed in dedicated laboratories. Publishing the EMLS methods and dataset might inspire similar initiatives to study across large geographic areas that will contribute to better understanding lake responses in a changing environment.Peer reviewe