An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling.

Sea surface temperature (SST), with its complex and dynamic behavior, is a major driver of ocean-atmosphere interactions. The purpose of this study is to investigate the behavior of SST and its prediction using a chaotic approach. Average mutual information (AMI) and Cao methods were used to reconstruct the phase space. The Lyapunov exponent and correlation dimension were used to investigate chaos. The Lyapunov exponent index was used to predict SST with a 5-year average prediction horizon using the local prediction method between 2023 and 2027. The results showed a 3-month delay time for the Pacific and Antarctic Oceans, and a 2-month delay time for the Atlantic, Indian, and Arctic Oceans. The optimal embedding dimension for all oceans is between 6 and 7. Our analysis reveals that the dynamics of SST in all oceans exhibit varying degrees of chaos, as indicated by the correlation dimension. The local prediction method achieves relatively accurate short-term SST predictions due to the clustering of SST points around specific attractors in the phase space. However, in the long term, the accuracy of this method decreases as the points in the phase space of SST can spread randomly. The model performance ranking with a Percent Mean Relative Absolute Error shows that the Indian Ocean has the best performance compared to other oceans, while the Atlantic, Pacific, and Antarctic and Arctic Oceans are in the next ranks. This study contributes to understanding the dynamics of SST and has practical value for use in the development of climate models.

A comprehensive study of the parameters affecting the stable isotopes in the precipitation of the Bangkok metropolitan area using model-based statistical approaches.

This study determined the main local and regional parameters affecting the stable isotopes (18O, 2H) in the Bangkok precipitation and developed the Bangkok meteoric water line (BMWL) (δ2H = (7.68 ± 0.07) δ18O + (7.25 ± 0.48)). First, Pearson correlation coefficients were used to determine the correlation between local and regional parameters. Six different regression methods were used based on Pearson correlation coefficients. The stepwise regression had the most accurate performance among them according to the R2 values. Second, three different methods were used to develop the BMWL, and their performances were also studied. Third, the stepwise regression method was used to study the effects of local and regional parameters on the stable isotope content of precipitation. The results showed that the local parameters had a greater effect on the stable isotope content than the regional ones. The stepwise models developed based on the northeast and southwest monsoons showed that moisture sources also affected the stable isotope content of precipitation. Finally, the developed stepwise models were validated by calculating the root mean square error (RMSE) and R2. This study demonstrated that the local parameters mainly controlled the stable isotopes in the Bangkok precipitation, while the regional parameters had a slight effect on them.

The deterioration of groundwater quality by seawater intrusion in the Chao Phraya River Basin, Thailand.

The Chao Phraya River Deltaic Plain is the largest basin in Thailand and the second largest one in Southeast Asia after the Mekong River Delta. In recent decades, the groundwater quality in the Lower Chao Phraya River Basin in Thailand has deteriorated due to salinization caused by seawater intrusion. In the present study, hydrogeochemical and statistical methods were employed to determine the hydrochemical characteristics of the groundwater and to investigate the possible sources of salinity in the study region for the years 2008 and 2020. In addition, samples were taken from precipitation, sea water, and river water to analyze their hydrochemical properties. Then, they were used as input in the "Simmr" code in the R programming language to model the hydrochemical conditions of the study area and their evolution over time. The results indicated that in the non-coastal regions, water-rock interaction (mineral weathering and ion exchange), and brine/connate water infiltration affected the quality of the groundwater. However, the seawater intrusion was limited only to the coastal regions. Furthermore, the groundwater quality deteriorated from 2008 to 2020. Finally, using stepwise regression in the R language, the salinity of the groundwater was simulated and compared with the measured salinity data. The results obtained by the stepwise model were in close agreement with those obtained from the hydrochemical studies. This study confirmed seawater intrusion in the coastal aquifer as well as the deterioration of groundwater quality over time. To slow down this process and to achieve sustainable conditions, groundwater extraction should be reduced in the study region.

Endemics determine bioregionalization in the alpine zone of the Irano-Anatolian biodiversity hotspot (South-West Asia).

Alpine habitats are characterized by a high rate of range restricted species compared to those of lower elevations. This is also the case for the Irano-Anatolian global biodiversity hotspot in South-West Asia, which is a mountainous area harbouring a high amount of endemic species. Using two quantitative approaches, Endemicity Analysis and Network-Clustering, we want to identify areas of concordant species distribution patterns in the alpine zone of this region as well as to test the hypothesis that, given the high proportion of endemics among alpine species, delimitation of these areas is determined mainly by endemic alpine species, i.e., areas of concordant species distribution patterns are congruent with areas of endemism. Endemicity Analysis identified six areas of concordant species distribution patterns irrespective of dataset (total alpine species versus endemic alpine species), whereas the Network-Clustering approach identified five and four Bioregions from total alpine species and endemic alpine species, respectively. Most of these areas have been previously identified using the endemic flora of different elevational zones. The identified units using both methods and both datasets are strongly congruent, proposing that they reveal meaningful distribution patterns. Bioregionalization in the Irano-Anatolian biodiversity hotspot appears to be strongly influenced by the endemic alpine species, a pattern likely to hold in alpine regions outside the Irano-Anatolian hotspot. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00035-021-00266-7.

Spatial distribution of stable isotopes (18O and 2H) in precipitation and groundwater in Iran.

Iran is a semi-arid and arid country which always faces a water shortage crisis. Thus, the water resources in Iran should be studied by accurate methods such as stable isotope techniques. In precipitation sampling stations across Iran, the δ18O (ranges from -16.3 to -0.3 ‰, -4.9 ‰ average), δ2H (-114 to -13 ‰, -24.2 ‰ average) and d-excess (-2.1 to -22.7, 16.5 ‰ average) values are higher compared to δ18O (ranges from -10.9 to -3.1 ‰, -6.7 ‰ average), δ2H (-71 to -6 ‰, -37.4 ‰ average) and d-excess (1.0 to -21.6 ‰, 14.9 ‰ average) values in groundwater stations. Stable isotope distribution maps in precipitation and groundwater were also developed for Iran. The stepwise technique was used to study the role of parameters influencing stable isotopes in Iran precipitation. Results show the dominant role of temperature, elevation and latitude as well as 'cP and MedT' air masses mixture on stable isotope values in precipitation. Furthermore, the contribution percentage of each air mass which influences Iran in groundwater resources recharge was studied using 'Simmr' package in R programming language. Finally, the accuracy of the developed stable isotope distribution maps was validated.

Monitoring of airborne asbestos fibers in an urban ambient air of Mashhad City, Iran: levels, spatial distribution and seasonal variations.

Asbestos, as with other pollutants in the air, has adverse effects on the health of human beings and animals. Today, the relationship between presence of asbestos fibers in the air breathed by humans and developing serious diseases such as lung cancer (asbestosis) and mesothelioma has been proven. The objectives of this study were to monitor the levels of asbestos fibers in ambient air of Mashhad, Iran during 2018, and to draw its Geographic Information System (GIS) distribution map for the city. In this descriptive study, 13 sampling points in Mashhad city were chosen. Sampling of asbestos was carried out for 3 hour during summer and winter at 2018. Sampling of asbestos was performed using MCE (Mixed Cellulose Ester) membrane filters (pour size 0.45 µm; diameter: 25 mm) and cassette holder and peripheral pump. The samples were the analyzed by the phase contrast microscopy (PCM) method (NIOSH7400). Also, to investigate the type of asbestos and for more accurate counting of fibers, Scanning Electron Microscopy (SEM) analysis was utilized. Meteorological parameter were recorded through portable devices. To draw the graphs, Excel, R and Arc GIS software were used. Results showed that the mean asbestos fiber concentrations were equal to 11.40 ± 2.14 and 14.38 ± 2.52 f/L in summer and winter, respectively. The maximum level of asbestos fiber was detected in the station of Baitolmoghaddas square by 26.64 ± 2.14 and 19.3 SEM f/L in winter and summer, respectively. High concentration of asbestos fiber observed in this study can be attributed to the heavy traffic, the presence of prominent industries in the vicinity of the study area, and topographic features. The results from this research recommends that suitable controlling policies should be regulated to reduce both ambient air asbestos and its adverse health endpoints in Mashhad.