Annual Average Values Research Articles

Spring wheat and triticale varieties productivity in risk farming area

The studies were carried out in 2015-2022 at the Far Eastern Research Institute of Agriculture (Khabarovsk Territory). The object of study is released varieties of spring wheat (Khabarovchanka, Lira-98, Anfeya) and spring triticale (Ukro, Rovnya, Grebeshok). Agrometeorological conditions during the years of research were varied: 2015, 2016, 2019, 2022 - with excessive moisture, 2021 - insufficient precipitation, 2017, 2018 - within the average annual values. As a result of the research, it was found that under favorable conditions, the yield of triticale varieties was 45.2-79.8 centners per hectare, under the influence of limited factors, this indicator decreases by 3.6-5.0 times, and the formation of the productivity of wheat samples in the region is more stable. (average yield was 33.6-51.4 сentners per hectare). In terms of grain quality, there were differences in grain crops - wheat varieties accumulate a high amount of protein in grain (more than 15 %), triticale samples have the highest content of lysine in grain (more than 400 mg/%). It was revealed that the signs of productivity in the zone of risky farming in triticale and wheat varieties are inversely correlated with agrometeorological environmental conditions. High correlations were noted between: yield and heat in July for the triticale variety Grebeshok (r=0.75), grain size and heat in May and July for samples of triticale Grebeshok and Rovnya (r=0.74 and r=0.73 respectively), yield and amount of precipitation in June for wheat varieties Khabarovchanka and Lira-98 (r= -0.77 and r= -0.78 respectively), weight of 1000 grains and surface air temperature for variety Lira-98 (r= - 0.78).

Screening of raw material for a spring soft wheat selection in the Middle Volga region conditions

The results of the study of 300 samples of spring soft wheat from a collection nursery with different ecological and geographical origin by identifying genetic sources with signs - precocity, short stemmed, coarse grained in the conditions of the Middle Volga region are presented. The research was carried out in 2019-2021 in the forest-steppe zone of the Samara region. The growing seasons during the years of research were quite arid: the hydrothermal coefficient in 2019 was 0.48, in 2020 - 0.52, in 2021 - 0.39 with an average annual value of 0.73. The study of the source material was carried out according to the methodology of the state variety testing and the methodological recommendations of the VIR. According to the results of the study, genetic sources of precocity were identified: Ural'skaya Kukushka, Chelyaba Rannyaya (Chelyabinsk region), Rifor 1, Rifor 6 (Leningrad region), Odeta, Libertina (Czech Republic), Chi Mai 1 (China); short stemmed: KWS Jetstream (Germany), Eleganza, Florens (France), Odeta, Libertina, Septima (Czech Republic), KWS Torridon (Great Britain), VZ-602 (Mexico), Iona (USA), Boett (Sweden), Long Fu 13 (China); coarse grains: Lutescens 6074/6-23 (Kinel), Ehkada 214, Ulyanovskaya 101, Burlak (Ulyanovsk), Saratovskaya 70 (Saratov), Chi Mai 1 (China). In turn, the samples Lutescens 6074/6-23, Ehkada 214, Burlak, in addition to high values of the mass of 1000 grains, had high yields during the years of research. The highest grain productivity was distinguished by samples of local breeding created in recent years - Kinelskaya Yubileynaya, Kinelskaya Zarya, Kinelskaya Zvezda, Kinelskaya 2020, Kinelskaya Volna, Lutescens 6074/6-23, Erythrospermum 6517/24-1, varieties of Bezenchyk selection - Tulaykovskaya 108, Tulaykovskaya 116, as well as Saratov selection - Saratovskaya 73, Saratovskaya 74, Albidum 33 and Ulyanovsk selection - Burlak, Ulyanovskaya 100. The selected genetic sources of valuable traits are recommended to be used as parental forms in breeding programs of crossing in the conditions of the Middle Volga region.

SEISMICITY of AZERBAIJAN and ADJACENT TERRITORIES in 2018–2019

In 2018–2019, the Azerbaijan network of seismic stations continued to operate without changes and consisted of 35 digital stations. To estimate the magnitude of earthquakes in these years, only the local magnitude ML Azr was determined. The total number of earthquakes recorded by the Azerbaijan network of stations was 5387 in 2018 and 6880 in 2019, however, the catalog of earthquakes in Azerbaijan and adjacent territories, published in the appendix to this article, provides data on earthquakes only with a magnitude of ML Azr3.0, which ОБЗОР СЕЙСМИЧНОСТИ 66 number was 101 in 2018 and 115 in 2019. The number N of the representative level earthquakes (KR≥9) in the study area slightly increased in 2018 and 2019 compared to the long-term average annual value Nav for 1993– 2017, while the released seismic energy decreased relative to the background level by 6 and 7 times, respectively. The most significant seismic event on the territory of the Republic in 2018 was the earthquake of June 5 with ML Azr=5.5, and in 2019 – the earthquake of February 5, ML Azr=5.2. Both events were felt in the nearest settlements with an intensity of Ii=6.

Long-term oscillations in the normalized biomass-size spectrum reveal the impact of oligotrophication on zooplankton trophic structure in the North Atlantic Subtropical Gyre

Ocean warming of the North Atlantic Subtropical Gyre (NASG) induced oligotrophication and a decrease in integrated net primary production during the 2010s, potentially affecting higher trophic levels. We analyzed long-term records (1994–2019) of daytime and nighttime zooplankton biomass in five size classes from the NASG. Daytime biomass decreased in the three largest size classes during the 2010s, while decrease in nighttime biomass was less evident due to the relative stability in diel vertical migrator biomass. We used the normalized biomass size spectrum (NBSS) to estimate the relative transfer efficiency between trophic levels. The steepness of the NBSS slope at the end of the time series increased by 14% (daytime) and 24% (nighttime) from the maximum observed annual average values (2011 and 2009, respectively). This suggests oligotrophication during the 2010s led to a significant reduction in the transfer of biomass across trophic levels, with negative impacts on the NASG planktonic food web.

Mathematical models to forecast temporal variations of power law shape parameters of a PV module working in real weather conditions: Prediction of maximum power and comparison with single-diode model

In this study, an innovative approach has been proposed to generate real time current–voltage characteristics and forecast peak power of photovoltaic (PV) modules working outdoors beneath real meteorological conditions. Power-Law Model (PLM) including four unknown parameters, two PV metrics (ISC, VOC), and two profile parameters (γ, m), has been used for fast, accurate and reliable modelling of PV modules. Explicit expressions giving γ and m parameters as functions of cardinal point coordinates (IMPP,VMPP, ISC and VOC) have been established. A daily monitoring of PV quantities, throughout one reference day, has been used to derive novel mathematical models of γ and m depending on module temperature and solar irradiance. To describe PV module behavior under changing meteorological conditions during an arbitrary day, mathematical models have been called to reproduce dynamical values of PLM shape parameters γ and m. Reliability of the approach has been assessed by comparing current–voltage characteristics and maximum power coming from PLM and Single-Diode Model (SDM) to experimental data of twenty-two PV modules measured at NREL. Comparison also reveals that I-V characteristics generated and predicted separately by PLM and SDM parameters are very close. In some cases, I-V characteristics generated by PLM parameters are more accurate. Furthermore, NRMSE values for generated and predicted I-V characteristics for all kinds of PV modules evaluated for different working conditions do not exceed 4% for both PLM and SDM, and determination coefficient values for long-term prediction of maximum power are close to one and annual average value is greater than 0,95.

A novel rainfall forecast model using GNSS observations and CAPE

Precipitable water vapor (PWV) and zenith total delay (ZTD) are highly correlated indicators used for forecasting rainfall effectively. These two factors are widely used when establishing qualitative and quantitative rainfall forecast models. Another indicator, however, convective available potential energy (CAPE), is also highly correlated with the occurrence of rainfall, but has not been combined with PWV or ZTD in previous studies. Therefore, a novel rainfall forecast model based on support vector regression that combines CAPE and Global Navigation Satellite System (GNSS)-derived PWV is proposed in this paper. Moreover, annual, seasonal scales, and hourly autocorrelation for predictors were also considered, and wavelet coherence (WTC) was introduced to further reveal the correlation relationships between CAPE, PWV and rainfall in both time and frequency domains. Hourly PWV, CAPE, temperature (T) and rainfall over a time span of three years were collected from four stations in Singapore and Taiwan to evaluate the performance of the proposed model. The root mean square error (RMSE) and mean absolute error (MAE) between the measured and forecasted rainfall were calculated for each year and averaged. Subsequently, the average RMSE and MAE were determined for the annual seasonal rainfall. The annual average RMSE value was 0.71 mm and the average annual seasonal RMSE value was 0.69 mm. The annual average MAE value was 0.21 mm and the annual seasonal was 0.22 mm. The average correlation coefficient (R2) for the annual value was 0.94 and the annual seasonal R2 value was 0.93. These results indicate that the forecast accuracy at the seasonal scale is basically consistent with annual scale. Additionally, a measure called comparable RMSE (CRMS) was introduced to evaluate the forecasting accuracy across all grades of rainfall. This analysis showed that the accuracy of moderate rainfall (MR) and heavy rainfall (HR) forecasts is similar, but slight rainfall (SR) forecasts are the most accurate among seasons and schemes.

Analysis of Air Pollutants and Their Potential Sources in Eastern Xinjiang, Northwestern Inland China, from 2018 to 2022

Air pollution in the developed regions of eastern China has been intensively investigated in the past decade. However, there is a relative dearth of air pollution studies on the northwest of inland China (e.g., Xinjiang). In this work, hourly measurement data of six criteria air pollutants (PM2.5, PM10, CO, NO2, O3, and SO2) for the past five years (2018–2022) from Hami and Turpan cities of eastern Xinjiang were analyzed to reveal air pollution characteristics and the distribution of potential sources. Hami and Turpan had the highest AQI values in winter due to increased coal combustion for domestic heating and unfavorable meteorological conditions. The slight elevations of AQI values in spring were caused by frequent dust storms. PM10 was the most frequent main pollutant in both Hami (63.1%) and Turpan (74.1%), followed by PM2.5 and O3. Except for O3, PM2.5, PM10, SO2, NO2, and CO exhibited a generally decreasing pattern in annual average values. But the annual average concentrations of PM10 in Hami (83.5 μg·m−3) and Turpan (139 μg·m−3) in 2022 were still higher than those in eastern China. Diurnal and monthly variations of the six criteria pollutants were influenced by a combination of emission sources and meteorological conditions. The air masses in eastern Xinjiang mainly originated from the west and north and were affected by both inter-regional and intra-regional transport. Analysis of the distribution of potential sources showed that local emissions strongly impacted particulate matter pollution in winter, while regional transport played a dominant role in other seasons. O3 showed a broad distribution of potential sources across all four seasons. Considering that the trend that O3 pollution increased year by year, eastern Xinjiang might face a similar pollution situation as eastern China, i.e., the combined pollution of particulate matter and O3.

Evaluating urban park ecosystem services and modeling improvement scenarios

Three ecosystem services of the 25 public parks in Bangkok, including carbon sequestration, avoided runoff, four air pollutant removals (CO, NO2, PM10, and PM2.5), and the relevant monetary values, were determined using i-Tree Eco software. Two modeling scenarios (MS) including MS1 (no greening improvement) and MS2 (improvement by increasing either green area or tree planting, or both in the parks) with tree annual mortality rates (AMR) of 1 and 3% were developed to forecast the parks' ecosystem services for 50 years after 2020 (2021–2071). The results revealed the synergistic interactions of the different tree planting specifications (MS1 and MS2) and tree mortality rates on the parks' ecosystem services. For MC2 with the assigned 1% AMR, the parks’ optimal ecosystem services were obtained and the average annual monetary value (0.55 million USD) of the total ecosystem services of the 25 parks over the 50-year forecast was 150% higher than that (0.22 million USD) in 2020. Based on MS1 and MS2, tree rotations should be conducted in the parks after 2057 and 2065, respectively, for the low tree AMR (≤1%) but not later than 2041 and 2043, respectively, for the higher tree AMR.

Hydrological Modeling and Evaluation of Water Balance Over the Complex Topography of Nile Basin Headwaters: The Case of Ghba River, Northern Ethiopia

Water resource evaluation, management, and conservation at the local, national, and international levels depend on an accurate understanding of the hydrological processes. In data-poor environments and topographically complicated areas like the Ghba subbasin in the headwaters of the Nile River, the function of hydrological models is crucial. The primary goal of this study is to use the WEAP model to simulate the hydrology of the Ghba basin. This is because recent hydrological behaviour has changed significantly and resulted in a serious water deficit. The minimal satisfactory performance limit for the monthly stream flow variable was strongly attained by the multi-variable calibration scenario (R2 = 0.82, NSE = 0.82, IA= 0.80 RSR = 0.87 and PBIAS = 9 % for calibration scenario; and R2 = 0.78, NSE = 0.81, IA= 0.70 RSR = 0.80 and PBIAS = 11.5 % for validation scenario). Evapotranspiration makes up 63.4% of the water balance, according to the model simulation, while surface runoff, interflow, baseflow and groundwater recharge accounting for 11.1 %, 11.8%, 5.4% and 8.3 %, respectively. The simulated average annual streamflow at the subbasin outlet is 16.33 m3/s. The simulated monthly minimum flow occurs in January with an average flow of 1.78 m3/s and a coefficient of dispersion of 0.45. Maximum flows occur in July and August, with an average flow of 53.57 m3/s and a coefficient of dispersion of 0.19. The main rainy season was shown to have a larger spatial distribution of simulated runoff, and the average annual recharge value is 53.5 mm. The study's conclusions indicated that both surface water harvesting and groundwater extraction might be used for reliable water distribution to the subbasin's continuously increasing sectoral water demand.

In vitro exposure to PM2.5 of olfactory Ensheathing cells and SH-SY5Y cells and possible association with neurodegenerative processes

PM2.5 exposure represents a risk factor for the public health. PM2.5 is able to cross the blood-alveolar and blood-brain barriers and reach the brain through three routes: nasal olfactory pathway, nose-brain pathway, blood-brain barrier pathway. We evaluated the effect of PM2.5 to induce cytotoxicity and reduced viability on in vitro cultures of OECs (Olfactory Ensheathing Cells) and SH-SY5Y cells.PM2.5 samples were collected in the metropolitan area of Catania, and the gravimetric determination of PM2.5, characterization of 10 trace elements and 16 polycyclic aromatic hydrocarbons (PAHs) were carried out for each sample. PM2.5 extracts were exposed to cultures of OECs and SH-SY5Y cells for 24-48-72 h, and the cell viability assay (MTT) was evaluated. Assessment of mitochondrial and cytoskeleton damage, and the assessment of apoptotic process were performed in the samples that showed lower cell viability.We have found an annual average value of PM2.5 = 16.9 μg/m3 and a maximum value of PM2.5 = 27.6 μg/m3 during the winter season. PM2.5 samples collected during the winter season also showed higher concentrations of PAHs and trace elements. The MTT assay showed a reduction in cell viability for both OECs (44%, 62%, 64%) and SH-SY5Y cells (16%, 17%, 28%) after 24-48-72 h of PM2.5 exposure. Furthermore, samples with lower cell viability showed a decrease in mitochondrial membrane potential, increased cytotoxicity, and also impaired cellular integrity and induction of the apoptotic process after increased expression of vimentin and caspase-3 activity, respectively.These events are involved in neurodegenerative processes and could be triggered not only by the concentration and time of exposure to PM2.5, but also by the presence of trace elements and PAHs on the PM2.5 substrate. The identification of more sensitive cell lines could be the key to understanding how exposure to PM2.5 can contribute to the onset of neurodegenerative processes.

Composition and selected properties of bio-waste collected in towns from single- and multi-family housing and in rural areas

The progress of civilisation contributes, among other things, to an increase in the mass of waste produced in households. A significant part of it is bio-waste (about 31% in Poland). It is generally agreed that bio-waste is a suitable substrate for valorisation through fermentation with biogas production. Designing new and optimising existing facilities, however, requires precise data on the composition of bio-waste and its properties, which is challenging due to seasonal variability, place of origin (single- or multi-family housing, urban or rural) and collection method.This paper presents the method adopted for conducting the study and the results of an annual, monthly analysis of the morphological composition and selected properties of bio-waste from source-segregated households from 4 rural municipalities and 4 cities, from neighbourhoods with single-family and multi-family housing in Poland.In household bio-waste, the proportion of food waste content ranges from 36.7 to 47.6% (annual average values). The proportion of edible food waste in relation to the total weight of food waste is 5 to 7 times lower. The yearly percentage of garden waste varied from 35.8 to 52.8%. A considerable amount of impurities (such as plastics, glass, and stones) is present in the bio-waste stream. The waste collected in containers in urban areas with multi-family houses is the most polluted (16.6%). The proportion of pollutants in bio-waste collected in bags (rural areas and cities with single-family housing) does not exceed 10%.

Medical expenditure trajectory and HbA1c progression prior to and after clinical diagnosis of type 2 diabetes in a commercially insured population in the USA

IntroductionMedical expenditures of individuals with type 2 diabetes escalate before clinical diagnosis. How increases in medical expenditures are related to glucose levels remains unclear. We examined changes in HbA1c and...

The Combined Effect of Atmospheric and Solar Activity Forcings on the Hydroclimate in Southeastern Europe

The purpose of this study was to analyze the influence of solar activity described by the sunspot number (SSN) on certain terrestrial variables that might impact the Southeastern European climate at different spatio-temporal scales (the North Atlantic Oscillation Index, NAOI, and the Greenland–Balkan Oscillation Index, GBOI—on a large scale; the Palmer Hydrological Drought Index, PHDI—on a regional scale; the Danube discharge at the Orsova (lower basin), Q, representative of the Southeastern European climate—on a local scale). The investigations were carried out for the 20th century using the annual and seasonal averages. To find the connections between terrestrial (atmospheric and hydrological) parameters and SSN, the wavelet coherence were used both globally and in the time–frequency domain. The analyses were carried out for the time series and considered simultaneously (in the same year or season), as well as with lags from 1 to 5 years between the analyzed variables. For the annual values, the type of correlation (linear/non-linear) was also tested using elements from information theory. The results clearly revealed non-linear links between the SSN and the terrestrial variables, even for the annual average values. By applying the wavelet transform to test the solar influence on the terrestrial variables, it was shown that the connections depend on both the terrestrial variable, as well as on the considered lags. Since, in the present study, they were analyzed using wavelet coherence, but only the cases in which the coherence was significant for almost the entire analyzed time interval (1901–2000) and the terrestrial variables were in phase or antiphase with the SSN were considered. Relatively few results had a high level of significance. The analysis of seasonal averages revealed significant information, in addition to the analysis of annual averages. Thus, for the climatic indices, the GBOI and NAOI, a significant coherence (>95%) with the solar activity, associated with the 22-year (Hale) solar cycle, was found for the autumn season for lag = 0 and 1 year. The Hale solar cycle, in the case of the PHDI, was present in the annual and summer season averages, more clearly at lag = 0. For the Danube discharge at Orsova, the most significant SSN signature (~95%) was observed at periods of 33 years (Brüuckner cycle) in the autumn season for lags from 0 to 3 years. An analysis of the redundancy–synergy index was also carried out on the combination of the terrestrial variables with the solar variable in order to find the best synergistic combination for estimating the Danube discharge in the lower basin. The results differed depending on the timescale and the solar activity. For the average annual values, the most significant synergistic index was obtained for the combination of the GBOI, PHDI, and SSN, considered 3 years before Q.

Structure and variations of global planetary boundary layer top from 2008-2022 multiple GNSS RO observations

The structural changes at the planetary boundary layer (PBL) top are very complex and closely related to climate and environmental changes. With the development of Global Navigation Satellite System Radio Occultation (GNSS RO), it provides a good opportunity to estimate and study PBL variations. In this paper, long-term variations and structures of planetary boundary layer height (PBLH) from 2008 to 2022 are investigated by the Wavelet Covariance Transform (WCT) method based on refractivity profiles from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Korea Multi-purpose Satellite-5 (KOMPSAT-5). Furthermore, the temperature and pressure of PBL top are detected using the temperature and pressure profiles from COSMIC and KOMPSAT-5 occultation data. The results demonstrate the latitudinal distribution of the PBL top height, temperature, and pressure, with the more apparent temperature's latitudinal characteristics. The pressure is more strongly correlated with PBL height than temperature. The PBL height in most land and oceans is high in summer and low in winter, and the PBL pressure is low in summer and high in winter, according to seasonal variation characteristics. The global PBL temperature is high in summer and low in winter, which is more obvious in high latitude region. A positive correlation between latitude and the magnitude of seasonal variation in PBL temperature is observed worldwide. Seasonal variation is less pronounced in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH) due to sea-land differences. The annual average value of the global PBL height, temperature, and pressure do not change significantly over the long term, and also there is no a discernible upward or downward trend.

Spatiotemporal evolution and driving factors analysis of the eco-quality in the Lanxi urban agglomeration

An analysis of the spatiotemporal evolution of ecological quality and its driving factors in the Lanxi urban agglomeration is important for ensuring environmental protection and high-quality, sustainable development in the region. We used moderate resolution imaging spectroradiometer (MODIS) remote sensing images to construct a remote sensing ecological index (RSEI) using principal component analysis (PCA) to reflect the ecological quality of the Lanxi urban agglomeration. The spatial and temporal characteristics and future changes in the RSEI of the study area from 2000 to 2020 were explored using the Sen and Mann–Kendall test and Hurst index, and the effects of natural and human factors on spatial variation in the RSEI of the study area were analyzed using Geodetector and geographically weighted regression (GWR) models. There were three main results. (1) In the past 20 years, the annual average RSEI value in the Lanxi urban agglomeration has been increasing at a rate of 0.0057/a, and areas of unsatisfactory ecological environmental quality have been reduced. (2) The Hurst index indicates that the majority of the area (47.54 % of the study area) will probably experience a degradation trend in the future, with 19.98 % of the area improving and random changes occurring in 9.45 % of the study area. (3) Vegetation type, soil type, and precipitation were the main reasons for the spatial differentiation in RSEI, land use type was the main human influence, and the influence of socioeconomic factors such as population density and gross domestic product (GDP) increased significantly. Vegetation, soil, and land use types were positively correlated with RSEI. The research results are of great significance for promoting the construction of an ecological civilization and coordinating a balance between social development and ecological environmental protection.

Estimation of the Angström coefficient over Ghardaïa city

Atmospheric turbidity over Ghardaïa city is investigated using the Angström coefficient. Its values are derived using five years (2004-2008) of data. They show a seasonal trend along the year with maximum values around summer months and minimum ones around winter months. The annual average value varied between 0.079 and 0.105. The daily average values were less than 0.1, between 0.1 and 0.2 and exceeded 0.2 for 64.6 %, 33.8 % and 1.6 % of the cloudless days respectively. This result reveals that for 64.6 % of the cloudless days the sky over Ghardaïa city was clean to clear, for 33.8 % was clear to turbid and for 1.6 % was turbid to very turbid.

Spatiotemporal characteristics of air pollutants and their associated health risks in '2+26' cities in China during 2016-2020 heating seasons.

To understand characteristics of air pollutants and their associated health risks in recent heating seasons in China, ambient monitoring data of six air pollutants in '2 + 26' cities in Beijing-Tianjin-Hebei and its surrounding areas (known as the BTH2+26 cities) during 2016-2020 heating seasons was analyzed. Results show that daily average concentrations of PM2.5, PM10, SO2, NO2, and CO dropped significantly in BTH2+26 cities from the 2016-2017 heating season to 2019-2020 heating season, while 8h O3 increased markedly. During 2016-2020 heating seasons, annual average values of total excess risks (ERtotal) were 2.3% mainly contributed by PM2.5 (54.4%) and PM10 (36.1%). With PM2.5 pollution worsening, PM10 and NO2 were the important contribution factors of the enhanced ERtotal. Higher health-risk based air quality index (HAQI) values were mainly concentrated in the western Hebei and northern Henan. HAQI showed spatial agglomeration effect in four heating seasons. Impact factors of HAQI varied in different heating seasons. These findings can provide useful insights for China to further propose effective control strategies to alleviate air pollution in the future.

Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model

Accurately and effectively estimating of the barrier layer thickness (BLT) is essential for the research of ocean thermodynamics, ocean dynamics, and air-sea interaction. Artificial intelligence model provides an effective means for accurately estimating BLT from sea surface and gridded Argo data. The present study focuses on the application of a hybrid particle swarm optimization-based artificial neural networks model (PSO-ANN) for estimating the BLT in the Indian Ocean. The input variables of the hybrid model include sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH) and precipitation (P), and the output variable is the BLT value. Multivariate satellite and gridded Argo data collected from the Indian Ocean between January 2012 and December 2019 (i.e., a database consists 239,568 training datasets and 34,224 testing datasets) are provided to prepare the training and testing datasets for the model. The parameters of ANN model, such as network parameter, network weights, and dropout rates are optimized using the PSO algorithm to achieve the best estimation model. R-squared (R2) and root mean square error (RMSE) are used to evaluate the performance of the model. Two groups of comparative experiments (Case 1 and Case 2) on the performance of the PSO-ANN model demonstrate that the model in Case 2 can better capture the complex features of the BLT in the ocean region. The performance of the PSO-ANN model in Case 2 is further compared with the data-driven estimation models such as the traditional ANN model and the known multilinear regression model (MRM), as well as the CESM2-WACCM dynamic model from CMIP6. The comparison results show that the dynamic model has the worst performance among the four models. Moreover, the annual average RMSE value for the PSO-ANN model is 1.83 m, which is 12% and 84% lower than that of the traditional ANN and MRM, respectively. The R2 value for the model of 0.85 is improved by 4% and 40% compared to that of two models. Furthermore, three regions with significant seasonal fluctuations of the BLT in the Indian Ocean are selected to further evaluate the estimation accuracy of the hybrid model in 2019; the Southeast Arabian Sea (SEAS), the Bay of Bengal (BoB), and the Eastern Equatorial Indian Ocean (EEIO). As a result, the hybrid model is capable of reflecting seasonal variation trends in these regions, but there is room for improvement in the estimation accuracy. In addition, the correlation analysis between BLT and sea surface parameters indicates that there exist significant correlations between the BLT and SSS, P. The results of this study show that the proposed hybrid model can be used to estimate and analyze BLT in certain regions with complex ocean dynamics processes. Moreover, the model can be extended to estimate other key ocean parameters and provide effective technical support for studying the internal ocean parameters.

Urticating macromedusae and stinging bathers on the South Atlantic coast: Oceanographic and climatological conditions of Olindias sambaquiensis (Müller, 1861) outbreaks

Incidents of bathers being stung by jellyfish on the coast of southern Brazil have been monitored from 2012 to 2022 to identify the species involved and to search for patterns of behavior in different summer seasons. For 10 years of monitoring, data on occurrence, density and the crossing with the abiotic variables of temperature and salinity of the water and wind direction were obtained. During the investigated period, seven species of macro jellyfish were recorded out, four of them were urticating species, in wich Olindias sambaquiensis was the most frequent and abundant. Based on physical-chemical data on the seawater in this area, the cohorts, and consequently the success in the recruitment and the density of the O. sambaquiensis, may be related with years of low salinity of these Coastal Water. This hydrozoan was also the only species that showed a correlation between average annual density values and incidents of bathers being stung in subsequent summers. Likewise, on a monthly time scale, incidents of stings were correlated with winds from the south quadrant that could promote the transport of organisms to the beach region. A predictive model of the occurrence of jellyfish stinging events is presented and applied for the summer of 2023.

Seasonal Monitoring of Water Quality Parameters and Pesticides at the Altınapa Reservoir Watershed

Altınapa Reservoir is a reservoir located on the Meram Stream in the Konya Province in Türkiye that supplies drinking water. The Altınapa Reservoir feeds the Konya Drinking Water Treatment Plant with 37.8 million m3 of water annually and the treated water is delivered by the drinking water network. The aim of this study was to determine the effect of agricultural activities and settlements on water quality in the Altınapa Reservoir Watershed. In the study, major water quality parameters (pH, electrical conductivity, total organic carbon, total nitrogen, nitrite, nitrate, phosphorus, phosphate, and total suspended solids) and pesticides were monitored at four stations (K1, K2, K3, K4) on the sub-streams feeding reservoir. Major water quality parameters were monitored at monthly intervals for 12 months between June 2020 and May 2021, and pesticides were monitored seasonally at 4 seasons (Fall 2019, Winter 2020, Summer 2020, and Spring 2021). A correlation matrix was used to assess the connections between nine indicators of water quality. The annual average conductivity and pH values of the samples taken from four different stations of the Meram Stream were 432 µS/cm and 8.04, respectively. The highest value for total nitrogen was 10.73 mg/L and it was 0.87 mg/L for nitrate. Annual average total organic carbon values were determined as 1.35, 1.69, 1.51, and 1.46 mg/L at K1, K2, K3, and K4 stations, respectively. Specific UV absorbance indicated that organic matters of water are mostly hydrophilic and have low aromatic content. In seasonal pesticide monitoring, 71 different compounds were detected in water samples. The compounds whose concentrations exceeded Turkish Environmental Quality Standards were identified as DDD-op, DDE-p.p', diflufenican, and imidacloprid. Pesticides constitute most of the micropollutants detected in water samples. The highest positive correlation among water quality parameters was obtained between conductivity and nitrate (0.81) and total nitrogen (0.88).