Erzsébet Kristóf

In the last two decades, the utilization of solar energy has been growing rapidly worldwide, mainly due to the increasing adoption of photovoltaic (PV) systems. Since solar energy is one of the most weather-dependent renewable energy sources, an increasing number of meteorological studies have focused on PV potential (PV_pot) and its projected changes under global warming. GCM outputs disseminated through the Coupled Model Intercomparison Project (CMIP) are often applied in energy-related urban climate studies, as they can be downscaled either statistically or dynamically. It is essential to evaluate raw (not bias-corrected) GCM data, which helps to determine the uncertainties in the GCM simulations before downscaling. Despite their coarse resolution, some studies even rely directly on the GCM grid cell time series to represent individual locations. Accordingly, this study evaluates 10 CMIP Phase 6 (CMIP6) GCMs with respect to some atmospheric variables (air temperature, solar radiation, and wind speed, which are the primary drivers of PV_pot) in four lowland grid cells representing four major urban areas in Central and Southeast Europe: Belgrade (Serbia), Budapest (Hungary), Vienna (Austria), and Prague (Czechia). The use of solar energy has increased significantly in most of these regions in recent years; however, it remains less studied than in Western Europe. ERA5 reanalysis is used as the reference dataset. We analyzed the boreal summer (JJA) days of three overlapping 30-year time periods: 1971–2000, 1981–2010, and 1991–2020. Our main findings are as follows: GCMs tend to overestimate solar radiation and underestimate maximum near-surface air temperature relative to ERA5 in all time periods and in all the four urban areas, which leads to a significant overestimation of the number of JJA days with high PV_pot (PV_pot,90). PV_pot,90 is increasing from 1971–2000 to 1991–2020 in the vast majority of GCMs, in all the four regions. EC-Earth3 and its different configurations (EC-Earth3-Veg, EC-Earth3-CC) are considered the most accurate GCMs relative to ERA5.

Organic fertilizers are widely used around the world, because they support the circular economy, sustainable agriculture, and improved soil quality, as well as carbon sequestration. State-of-the-art, process-based models can simulate the environmental impacts of organic fertilizer use and can address issues like the effect of fertilizer amount and type on crop production, soil fertility, soil organic matter accumulation, nitrate leaching, and greenhouse gas emission. However, the lack of information on the proper attribute settings for fertilizer inputs in the models hampers their application. In this study, the main goal was to support the setting of organic fertilizer attributes for process-based model applications. A comprehensive data collection was performed to gather organic fertilizer attributes that are relevant for the carbon and nitrogen cycle-related simulations. Based on the literature search, representative values are presented that can be instantly used in the models as generalized settings for several farmyard manure and slurry types. We also addressed the question of how fertilizer attribute-setting-related uncertainties propagate to the simulation outputs. We used the Biome-BGCMuSo biogeochemical model for that purpose with a maize monoculture simulation. The results indicate that manure type specific attribute setting is crucial for the nitrogen balance related model variables. For soil nitrous oxide efflux, improper composition settings can severely distort the simulation results. Sensitivity analysis suggested that dry matter content and organic nitrogen content are the two most important manure attributes that modellers must properly adjust. For slurry, the dry matter and ammonium content must be constrained for proper simulation results. The study supports crop and biogeochemical model setup with ready-to-use pragmatic information.

During the last decades, development of novel low-cost sensors commercialized for indoor air quality measurements has gained interest. In this research, three AirVisual Pro air quality monitors were used to monitor PM_2.5 and carbon dioxide concentrations in which two were installed indoors and one outdoors at two residential apartments in Central Europe (Budapest, Hungary). In our research, we present a methodology to support the evaluation of indoor sensors by utilizing official outdoor monitoring data, leveraging the fact that indoor spaces are frequently ventilated and thus influenced by outdoor conditions. We compared six-year measurement data (January 2017–December 2022) with outdoor concentrations provided by the Hungarian Air Quality Monitoring Network (HAQM). However, the well-known low spatial representativeness and high spatio-temporal variability of PM_2.5 in city environments made this evaluation problematic, which needed to be addressed before comparison. Here we quantify the spatial heterogeneity of the HAQM PM_2.5 data for a maximum of eight stations. Then, based on the carbon dioxide readings of the AirVisual Pro units, data filtering was performed for the AirVisual 1 and AirVisual 2 sensors located in indoor environments to identify ventilated periods (nearly 10,000 ventilated events) for the AirVisual 1 and AirVisual 2 sensors, respectively, for the comparison of indoor and outdoor PM_2.5 concentrations. The AirVisual 3 sensor was placed in a garden storage, and the measurements taken there were considered outdoor values throughout. Finally, four heterogeneity criteria were set for the HAQM data to filter conditions that were assumed to be comparable with the indoor sensor data. The results indicate that the spatial heterogeneity was indeed detectable, and in approximately 50–60% of the cases, the readings could be considered as non-representative to single location comparison, but the results depend on the selected homogeneity criteria. The AirVisual and HAQM comparison indicated relatively low sensitivity to heterogeneity criteria, which is a promising result that can be exploited. AirVisual sensors generally overestimated PM_2.5, but this bias could be corrected with a simple linear adjustment. Slopes changed across sensors (0.83–0.85 for AirVisual 1, 0.48–0.53 for AirVisual 2, and 0.70–0.73 for AirVisual 3), indicating general overestimation and correlations from moderate to high (R² = 0.45–0.89) depending on the device. In contrast, when we compared the measurements only with data from the nearest reference station, we obtained a weaker match and slopes that did not match those calculated by taking into account homogeneity criteria. This research contributes to the proliferation of citizen science and supports the application of LCSs in indoor conditions.

To reduce dependence on fossil fuels, and achieve climate protection goals, the global renewable energy market is expanding rapidly. With ongoing technological advances, the use of solar power is increasing throughout the year, even during the boreal winter, when large-scale atmospheric circulation phenomena – such as the North Atlantic Oscillation (NAO) – are the main drivers of the climate of Europe. Consequently, the NAO has a significant impact on the variability of solar power, particularly in regions such as Southwestern Europe (Jerez et al., 2013).In the Carpathian Region in East-Central Europe, the NAO has a significant influence on the climate in wintertime. Previous studies have shown that negative NAO phases correspond to cold and wet winters, while positive NAO phases are typically associated with warm and dry winters (Bartholy et al., 2010). However, the effect of NAO on the photovoltaic power potential (PVpot) has not been extensively studied in this region.This study investigates the influence of the NAO on PVpot over East-Central Europe with a special focus on the Carpathian Region and examines its projected changes in wintertime. Since large-scale atmospheric processes can be reproduced by general circulation models (GCMs), we analyze the historical (1981-2010) and near-future (2031-2060) GCM outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) with horizontal resolutions around 1°. To assess the influence of the NAO on PVpot, “mobile NAO index” (Portis et al., 2001) will be obtained from the GCM outputs, which shows the most intense regions of the NAO. For comparison reasons, ERA5 reanalysis will also be used.With the installed capacity of solar power is expected to continue increasing in East-Central Europe, our results provide an insight into how projected changes in large-scale atmospheric circulation may affect near-future photovoltaic power production. The research was funded by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014).Bartholy et al. (2009). Applied Ecology and Environmental Research, 7(3), 229–240.Jerez et al. (2013). Journal of Applied Meteorology and Climatology, 52(10), 2204-2225.Portis et al. (2001). Journal of Climate, 14(9), 2069–2078.

The climate of the Hungarian lowland (Central European region, Pannonian Plain area) can be characterized by Köppen’s Cfb climate formula (C—warm temperate, f—no seasonality in the annual course of precipitation, b—warm summer). This characterization does not provide information about the human thermal load and thermal perception. The aim of this work is to fill this gap. We focused on the morning, clear-sky periods of the day, when the heat supply provided by the weather is the lowest. The human thermal load of clear-sky mornings was estimated using the new clothing thermal resistance–operative temperature (r_cl–T_o) model. In contrast to IREQ-type (Required Clothing Insulation) models, this model parametrizes the total metabolic heat flux density (M) as a function of anthropometric data (body mass, height, sex, age). In the simulations, the selected persons walk (M values range between 135 and 170 W m⁻²) or stand (M values range between 84 and 96 W m⁻²), while their body mass index (BMI) varies between 25 and 37 kg m⁻². The following main results should be highlighted: (1) Human activity has a significant impact on r_cl; it ranges between 0 and 3.5 clo during walking and between 0 and 6.7 clo during standing. (2) The interpersonal variability of r_cl increases with increasing heat deficit accordingly; in the case of a walking person, it is around 1 clo in the largest heat deficits and around 0 clo in the smallest heat deficits. Since, in general, anticyclones increase the heat deficit while cyclones reduce it, extreme thermal loads are associated with anticyclones. It should be mentioned that the interpersonal variability of the human thermal load cannot be analyzed without databases containing people’s anthropometric data.

We investigated the human thermal load in Martonvásár (Hungarian lowland, Carpathian region) in anticyclonic weather conditions in the morning, when a) the sky was completely clear and on the other hand, when b) there was fog. A customizable clothing thermal resistance-operative temperature model was used. The body mass index and the basal metabolic flux density values of the person in the simulations were 25 kgm-2 and 40 Wm-2, respectively. During the observations, weather data was provided by the automatic station of the HungaroMet company and it was accessible on the company's website. We had 77 observations in foggy weather, while we had 46 observations under clear sky conditions in the period between 2019–2023. The following main results should be highlighted: 1) clothing thermal resistance (rcl) varied between 0.5-2.5 clo in the case of fog, while in clear-sky cases rcl was between 0.9-3.5 clo. 2) Based on our data analysis, we estimated that the warming effect of the morning fog was around 0.8 clo. 3) We also showed that the effect of inter-personal variability on rcl was significant when the heat deficit was high (rcl ≥ 2.5 clo) and at this time it was comparable with the degree of the warming effect of fog. It should be mentioned that the analysis of typical weather situations from the point of view of human thermal load is a new field of research, since there is little information available on this subject.

We analyzed the human thermal load of summer weather in the Cfb climate based on the results of a new model based on the human body energy balance equation and the skin surface evaporation gradient formula. The active surface of the model is the skin surface, the person is lying in a resting position, its skin type is Fitzpatrick skin type IV. For that purpose, longitudinal research method was performed in 2022 in Martonvásár, Hungary (East-Central Europe), comprising 331 observations in which weather conditions and thermal sensation types were recorded simultaneously. The main observation is that in warm climates and/or weather situations, the amount of thermal load can be very simply characterized by latent heat flux density values of the skin evaporation. From a human point of view, the most important characteristics of summer weather in the Cfb climate are as follows: 1) The latent heat flux density of skin surface evaporation varied between 10 and 300-350 Wm-2, while the operative temperature ranged between 25 °C and 80 °C. 2) The relationship between skin surface evaporative resistance and operative temperature can be characterized by an exponential function. In cases of thermal sensation type "neutral", skin surface evaporative resistance values are mostly above 0.5 hPa·m2·W-1. Observations made by people with different skin types are essential to generalize the results.

A new clothing resistance model is described and applied to characterize the human thermal and moisture climates. The climate was described by the thermal (rcl,t) and evap-orative (rcl,e) resistances of comfortable, imaginary clothing. The key input variable of the model is the rate of sweating (λEsw). Since sweating is an individual-specific process, the model applies to the individual. The observations were carried out by a single individual in Martonvásár (Hungarian lowland, Central Europe) in the summer of 2023 and the winter of 2024. During the observations, the observer walked at a speed of 1.1-1.7 ms-1 in weather-appropriate clothing. Among the results, we would highlight the following: Dur-ing large environmental heat surpluses, the rcl,t values were found to be between 0.1-0.5 clo-t (clo-t = 0.155 m2·℃·W-1), while the rcl,e values fell between 0.4-0.8 clo-e (clo-e = 0.155 m2·hPa·W-1). The values of the parameters close to 0 were caused by intense sweating (λEsw > 200 Wm-2). The applicability of the model is limited and largely depends on the rela-tionship between λEsw and the metabolic heat flux density.