Spatial-temporal Variations Research Articles

Spatial–temporal variation and source analysis of heavy metals in different land use types in Beilun District (2015 and 2022)

The soil environment plays an important role in urban ecosystems. To study the heavy metal contamination of soil in Beilun District, Ningbo, we collected soil samples from 60 points in urban and peri-urban areas of Beilun District and analyzed the spatiotemporal variation and sources of heavy metal pollution in various land-use types. The results shown that the heavy metal contents in 2015 and 2022 were higher than the background soil values of Ningbo city, and there was an accumulation of heavy metals over these 7 years. The contents of heavy metals in green belts and woodland in 2022 were higher than those in 2015, while there was no significant change in agricultural land. The heavy metal contents in both years were mainly in the order green belts > agricultural land > woodland. The spatiotemporal distribution of heavy metal content showed that heavy metal pollution in Beilun District was concentrated in five industrial areas, and there was a trend toward the disappearance of highly polluted points. But the single-factor pollution index, pollution load index (PLI), and geoaccumulation index (Igeo) indicated that there was no significant heavy metal pollution in Beilun District, and individual elements at specific points showed slight pollution. The source analysis results showed that the main source of Hg is chemical, As is mainly derived from agricultural, Cr, Ni and Cu are mainly derived from natural, the main sources of Zn and Cd are electroplating and machinery activities, and the main source of Pb is traffic. These results specify a reference for future investigation on urban soil heavy metals, and the source apportionment results provide a scientific foundation for subsequent soil heavy metal pollution treatment.

Thermal and precipitation variability across four seasons in Poland (1951–2020)

Abstract This paper shows spatial variability of temperature and precipitation conditions across the four seasons in Poland and examines long-term trends in these parameters. The spatial variability of temperature in Poland is caused primarily by the clash of oceanic air masses from the west and continental air masses from the east. The spatial distribution of precipitation depends strongly on the relief and altitude above sea level. The seasons are generally getting warmer. However, average temperatures increase over time at different rates and levels of significance. Except in winter, no statistically significant trends were found in the seasonal precipitation totals. Seasons are periods when certain climatic conditions prevail. Depending on the criteria adopted, different types of seasons can be distinguished, such as meteorological, thermal, and calendar seasons. These are considered in this research. Consequently, different time frames of seasons result in varying values and patterns of meteorological characteristics. In this research, this concept is presented using Poland as an example.

Impact of the Summer Atmospheric Heat Source over the Tibetan Plateau on Interannual Variability of Meridional Circulation on the North Side of the Tibetan Plateau

Abstract The summer atmospheric heat source (AHS) over the Tibetan Plateau (TP) induces meridional circulations in TP and its surrounding areas. Previous studies mainly focused on the monsoon circulation on the south side of TP, while the formation and maintenance mechanisms of meridional circulation on its north side remain unclear. This study compared three calculation methods of the AHS, analyzed the spatial–temporal variability of the summer AHS over the TP, and discussed its influence on the interannual variability of meridional circulation on the north side of the TP based on the two-dimensional decomposition method of atmospheric circulation and sensitivity experiments. The results indicate that in the positive AHS anomalies years, the diabatic heating of condensation latent release in southeastern TP could motivate anomalous ascending motion. Simultaneously, the increased meridional temperature gradient between the mid- and high latitudes of East Asia leads to an enhanced southward westerly jet. In this context, the region on the north side of TP, located on the north side of the westerly jet entrance, is affected by negative anomalous relative vorticity advection, prevailing anomalous descending motion, which makes the descending branch of meridional circulation significantly presented. Unlike previous studies that considered the descending branch of meridional circulation as the compensation for upward flow, the results of the linear baroclinic model (LBM) verify that the descending branch is mainly influenced by the vorticity advection related to regional scale variability of the westerly jet. This study reveals the physical mechanism of meridional circulation on the north side of TP, which offers valuable implications for seasonal forecasting in TP and Northwest China.

River linear inversion to assess drainage base-level fall history in North-western Apennines and implications on the Alessandria Basin tectonic activity

Drainage network systems are responsive elements to recent active tectonics from among all the topographic features. In geodynamically active areas, fluvial landscapes can record different processes through the formation and current presence of features related to spatial-temporal variation in base-level fall and vertical incision of stream channels. This study focuses on the tectonic evolution of the Alessandria Basin, a synorogenic tectonic basin located at the junction between the Alps and the Apennines, that experienced progressive subsidence during the overthrusting of the Monferrato Thrust Front (the westernmost outer arc of the Apennine belt) onto the Po Foreland Basin. Even though several studies have assessed the Neogene tectonic evolution at a regional scale, rates and timing of the Quaternary activity are still poorly understood in terms of both Alps/Apennines uplift and activity of the compressive front of the Monferrato Arc. In this paper, we applied the method of the river profile linear inversions to reconstruct the base-level fall history of 6 catchments that drain into the Alessandria Basin. We used nine 10Be-derived basin-average denudation rates to constrain the erodibility parameter needed to infer base-level fall rates from χ-transformed river profiles. The results describe the tectonic history of the area in the last ∼5 Ma, documenting increases in base-level fall rate with an initial peak between 3 and 2.5 Ma, and a second between 2 and 1.5 Ma. While the first peak is coeval with the uplift phase that involved most of the northern-central Apennine, the second one suggests an acceleration in subsidence of the Alessandria Basin concurrently with the uplift of the Monferrato Thrust Front.

Enhanced comprehension of the cross-scale forming mechanism in CF/PEEK resistance welding: Analysis of temperature gradients and meso–microscopic phase transitions

Resistance welding technology is an effective solution for connecting thermoplastic composites. However, limited research on non-uniform distribution of interfacial temperature and complex evolution mechanism of phase-change molding hinders its application. This reliance on traditional experimental and trial-and-error methods severely impedes its development. Therefore, this study employed a novel cross-scale numerical simulation method, being applied for the first time in the field of resistance welding for elucidating the underlying mechanism of joint formation through multi-component phase transformation. The constructed model accurately represented the transient heat transfer of a realistic wire mesh structure in three dimensions, while incorporating complex gradient effects arising from both spatial and temporal variations in heating element temperature due to air interference. Further, melting, flow, and consolidation behavior of the resin matrix in resistance welding of carbon fiber/polyetheretherketone (CF/PEEK) thermoplastic composite was investigated herein at a mesoscopic scale. To accomplish this objective, a three-phase flow spatial forming and evolution model for resistance welding was developed at the mesoscopic scale by integrating principles from fluid dynamics. Interestingly, introduction of an air phase reinstated the spatial voids formation and flow in resistance welding, providing robust evidence for the initiation mechanism of internal defects. Moreover, this study revealed variations in the melting mode of PEEK resin across different regions within the weld and highlighted distinct distributions of voids resulting from uneven heat transfer. The interfacial phenomenon and flow diffusion mechanism between components in resistance welding tests were further verified and discussed through analyzing scanning electron microscopy and energy dispersive spectroscopy results. Temporal and spatial correlation in void distribution within the welding zone was observed, which was found to be consistent with simulation results. Specifically, with the progress of the welding process, air near the wire mesh gradually dispersed into the laminate, with significantly more voids at weld seam edges than in central regions. This research methodology based on stepwise scale reduction offers a promising avenue for investigating resistance welding and other connection forming applications, in particular, within the context of engineering underlying logical behavior.

Spatial-temporal neural networks for catch rate standardization and fish distribution modeling

Catch-per-unit-effort (CPUE) standardization is crucial for fishery stock assessment but often presents challenges due to spatial-temporal variations in species distribution and fishing effort. In this simulation study, we propose the use of customized artificial neural networks (ANNs) for modeling the spatial-temporal variations in CPUE standardization. This is achieved by encoding prior knowledge of the dependency structure between the variables into the architecture of the ANNs. We conducted numerical experiments on simulated data to compare our customized ANNs with Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and fully connected ANNs used in previous studies. Our simulated data cover three spatial-temporal dynamics scenarios with different degrees of species distribution shift over time: (1) steady fish distribution; (2) gradual directional shift over time; (3) sudden directional shift. In predicting the standardized CPUE in this simulation study, the customized ANNs demonstrated greater accuracy compared to the commonly used fully connected ANNs with an error reduction of over 70 %, more than 80 % compared to GLMs, and more than 40 % compared to GAMs, in terms of an error metric called the scaled mean absolute relative error. Our findings suggest that customized ANNs can serve as an alternative modeling tool alongside GLMs and GAMs in fisheries modeling.

Population Pressure and Changing Land Use Land Cover in Morigaon District, Assam, India - A Geospatial Analysis

Objective: This study intends to prioritize the underlying mechanisms underpinning the spatial-temporal variations in Land Use and Land Cover (LULC) change in the Morigaon district, located in the middle part of Brahmaputra valley of Assam, India covering an area of 1498 km2.At the same time, this study focuses on the impact of population pressure on changing LULC in the study area. Methods: This research adopted geospatial approach for analysing land use land cover change in the study area. Satellite images from the years 1992 to 2022 have been utilized to identify the land transformation. The analysis of the land transformation has taken into account the five main LULC classifications of agriculture, forest, water bodies, built-up lands, and sand deposits. It has been discovered that the LULC classes identified in this study, which comprise both natural and anthropogenic elements, have a very strong link with the population growth in the Morigaon district. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Findings: Water bodies, forests and agriculture have significantly decreased by 0.13%, 4.66% and 1.64% respectively due to substantial anthropogenic activity during the past 30 years. In contrast, sand deposits and built-up lands increased by 3.13% and 3.30%. To show the relationship between population expansion and LULC indicators Pearson correlation method is used. Very significant relationships have been found between population and built-up lands with rho value 0.95. The relationship between population with agriculture, forest, water bodies and sand deposits have been seen with rho value -0.51, -0.89, -0.64 and 0.97 respectively. The loss of the geographic extent of natural classes like water bodies, forests, signifies that the geometrical progression in the human population has greatly amplified anthropogenic activities. Novelty: The novelty lies in adopting geospatial techniques that would certainly enhance land resource conservation. Environmental concerns have been increasing due to significant land surface change, which is predominantly the result of human-induced activities. In the studied area, socioeconomic, demographic, and proximity factors have a significant impact on the LULC change processes. The findings can be utilized to forecast potential LULC scenarios and offer direction for land management strategy. Keywords: LULC, Satellite Images, Anthropogenic, Pearson Correlation, Proximity Factor

IRSEI-based monitoring of ecological quality and analysis of drivers in the Daling River Basin

The Daling River Basin is an important ecological functional area in the western region of Liaoning with outstanding environmental problems. The monitoring of ecological and environmental quality in the basin and the analysis of driving factors are of great importance for the protection of the ecological environment and the improvement of economic quality. In this paper, the three periods of Landsat remote sensing images in 1995, 2010 and 2020 are used as the basic data, and platforms and technical means such as RS and GIS are used to decipher and extract the three periods of land use information, and to construct the land use type transfer matrix. The remote sensing ecological index (RSEI) was improved, and the principal component analysis method was applied to construct the improved remote sensing ecological index (IRSEI) model based on the greenness (NDVI), moisture (WET), heat (LST) and new dryness (N-NDBSI), so as to realize the dynamic monitoring of ecological and environmental quality in the study area. Based on the land use change, combined with the trend of improved remote sensing ecological index (IRSEI) of Daling River Basin, thus achieving the purpose of rapid and efficient dynamic monitoring of ecological quality of Daling River Basin from 1995 to 2020. A geoprobe model was then used to systematically assess the drivers of ecological quality in the catchment. The results show that the improved remote sensing ecological index (IRSEI) can efficiently and accurately obtain the spatial distribution pattern and temporal variation trend of IRSEI in the study area, which is more in line with the characteristics of indicators in this study area. The IRSEI in the study area showed an increasing trend from 1995 to 2020, from 0.4794 to 0.5615, and the proportion of benign ecological classes increased year by year during the period. Among the evaluation indicators, NDVI and N-NDBSI are the main factors affecting the environmental and ecological quality of the Daling River Basin, and the increase of vegetation cover, climate regulation and human activities have obvious promoting effects on the improvement of the ecological environment of the Daling River Basin. This study provides a scientific theoretical basis for the implementation of further ecological environmental protection measures.

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery. A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database. Multi-correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates. The MLC positional deviation and effective impact factors show a strong multi-correlation (R=0.701, p-value<0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5-fold cross-validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235cGy, while the relative volume deviation remained small at 0.470%-5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of -0.506 to -0.720 and p-value<0.05) but marginally with dosage deviations (with correlation coefficient<0.498 and p-value>0.05). The RF predictive model provides a prior tool for VMAT quality assurance.

Evaluation Method for Underwater Ultrasonic Energy Radiation Performance Based on the Spatial Distribution Characteristics of Acoustic Power.

In recent years, underwater wireless ultrasonic energy transmission technology (UWUET) has attracted much attention because it utilizes the propagation characteristics of ultrasound in water. Effectively evaluating the performance of underwater ultrasonic wireless energy transmission is a key issue in engineering design. The current approach to performance evaluation is usually based on the system energy transfer efficiency as the main criterion, but this criterion mainly considers the overall energy conversion efficiency between the transmitting end and the receiving end, without an in-depth analysis of the characteristics of the distribution of the underwater acoustic field and the energy loss that occurs during the propagation of acoustic waves. In addition, existing methods focusing on acoustic field analysis tend to concentrate on a single parameter, ignoring the dynamic distribution of acoustic energy in complex aquatic environments, as well as the effects of changes in the underwater environment on acoustic propagation, such as spatial variability in temperature and salinity. These limitations reduce the usefulness and accuracy of models in complex marine environments, which in turn reduces the efficiency of acoustic energy management and optimization. To solve these problems, this study proposes a method to evaluate the performance of underwater ultrasonic energy radiation based on the spatial distribution characteristics of acoustic power. By establishing an acoustic power distribution model in a complex impedance-density aqueous medium and combining numerical simulation and experimental validation, this paper explores the spatial variation of acoustic power and its impact on the energy transfer efficiency in depth. Using high-resolution spatial distribution data and actual environmental parameters, the method significantly improves the accuracy of the assessment and the adaptability of the model in complex underwater environments. The results show that, compared with the traditional method, this method performs better in terms of the accuracy of the acoustic energy radiation calculation results, and is able to reflect the energy distribution and spatial heterogeneity of the acoustic source more comprehensively, which provides an important theoretical basis and practical guidance for the optimal design and performance enhancement of the underwater ultrasonic wireless energy transmission system.

The Influence of Land Cover on Spatio-Temporal Variation of Air Temperature in Grogol District, Central Java – Indonesia

As an urban fringe of Surakarta City-Indonesia, Grogol District is experienced by increasing temperature. The purpose of this study is: 1) to determine the spatial and temporal variations of air temperature in the Grogol District and 2) to determine the difference in air temperature between built-up land cover and non-built-up land cover, particularly rice fields. Air temperature data was obtained by temperature data logger from four sample points on October 27th to December 13th, 2021. Air temperature and land cover data were analyzed by descriptive and inferential statistics as well as spatial interpolation on air temperature data. The result indicates that higher air temperatures are spread over the northern part which is dominated by settlements. Lower air temperatures are found in the southern part which is dominated by rice fields. The temporal variation of air temperature at the four sample points shows almost the same pattern. However, the air temperature was higher in the built-up land cover than in the rice fields. The statistical difference test also showed that there were differences between the two land covers. In conclusion, Grogol District is affected by the urban heat island phenomenon from Surakarta City. This is particularly related to land cover change and increasing population. Therefore, to prevent the increasing temperature, the protection of the open greenspace area should be implemented.

Lightning-induced fire regime in Portugal based on satellite-derived and in situ data

Fire databases typically contain information regarding the location, timing, and duration of fire occurrences, as well as the cause (natural or human-induced). These databases allow for the analysis and understanding of the circumstances surrounding the ignition and propagation of wildfires, being highly relevant when addressing fire suppression and management plans, or for improvement of prevention policies. In Portugal, a large number of fires in the official database have unknown causes, which limits the understanding about the relevance of each fire cause, in particular the role of lightning on the ignition of fires over the country. The objective of this paper is twofold: first, we investigated the spatial-temporal variability of lightning-induced fires from 2003 to 2020. Then, we evaluate the feasibility of using remote sensing data as a surrogate for identifying fire causes, through validation with the fire database. Our results revealed that lightning-ignited fires represent an even smaller fraction of all fire events than mentioned in the latest official report about fire causes in the country, accounting for only 1 % of the fire occurrences and 4.6 % of the total burned area in the 18-year period under analysis. The months of June to September comprise 91 % of all lightning fires, which occur more frequently in the northern, remote regions of the country and at relatively high altitudes. Moreover, lightning ignitions originate fires larger than the global average and contributed significantly to the total area burned during the extreme 2003 and 2017 fire seasons. Its importance in these extreme years suggests that lightning can trigger large fires when in conjunction with compound events such as droughts and heatwaves. When compared with in-situ databases, the application of remote sensing data reproduced the seasonality of lightning-ignited fires, but failed to account for the contribution of smaller fires, which represent the majority of occurrences in the Portuguese fire database. Finally, we discuss the implications of our findings for the improved assessment of fire risks, causes, and impacts in Portugal.

High-resolution spatiotemporal inference of urban road traffic emissions using taxi GPS and multi-source urban features data: a case study in Chengdu, China

The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms—Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)—are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis.

Long-Term Analysis of Spatial–Temporal Variation in Ecological Space Quality within Urban Agglomeration in the Middle Reaches of the Yangtze River

Long-Term Analysis of Spatial–Temporal Variation in Ecological Space Quality within Urban Agglomeration in the Middle Reaches of the Yangtze River

Temporal-spatial variability of grazing behaviors of yaks and the drivers of their intake on the eastern Qinghai-Tibetan Plateau.

Grassland-livestock balance is an important principle of sustainable development of grassland livestock production and grassland ecosystem health. Grassland degradation becomes more serious at global scales and especially at the area that is sensitive to climate change and human activities. Decreases in pasture biomass and shifts in plant community composition in degraded grasslands can largely affect grazing behaviors of livestock. Up to date, however, it is unclear that whether livestock behaviors change across spatial and temporal scales and what key factors are to shape observed behavioral patterns of livestock. Here, yak behaviors including grazing, rumination and walking on the eastern Qinghai-Tibetan Plateau (QTP) were monitored by a continuous visual observation, to investigate temporal and spatial variations of grazing behavior of yaks (Bos grunniens); based on the data from public database in the past 18 years, a meta-analysis was conducted to examine the main factors that affect grazing behaviors and intake of yaks. We showed that grazing behaviors of yaks differed significantly within hours, among hours of each day and among days as well as across different observation sites. Intake rate of yaks was higher in the morning than in the afternoon, but walking speed showed an inverse trend compared with intake rate. Resting, altitude, the mean annual precipitation (MAP), the mean annual temperature (MAT), forage ash, yak age and season were the main predictors for yak intake, and forage and yak individual characteristics had direct effects on grazing behaviors and intake of yaks. The findings confirm that grazing behaviors of yaks can vary even at small temporal scales and regional scales, which is closely related to the shift in forage quality and biomass caused by environmental changes. The study suggests that multiple factors can be responsible for the variation in livestock behaviors and shifts in behavioral patterns may consequently lead to positive or negative feedback to grassland ecosystems through plant-animal interactions.

Surface Ozone in the Huaihe River Economic Belt, China: Spatial-Temporal Variations and Meteorological Driving Force

Surface Ozone in the Huaihe River Economic Belt, China: Spatial-Temporal Variations and Meteorological Driving Force

Spatial and Temporal Distribution of Antibiotic Resistance Genes in Different River Sediments in Typical Cities: A Case Study of Shijiazhuang

Rivers are important reservoirs of antibiotic resistance genes (ARGs). However, most current studies have focused on the temporal and spatial distribution, and data on the differences in the species and abundance of ARGs between urban and rural rivers is still lacking for certain areas. In view of this, two rural rivers and three urban rivers were selected in Shijiazhuang City. In both December 2020 and April 2021, sediments were collected at 15 sampling sites. Metagenomic sequencing technology was used to compare the differences in temporal-spatial variation for ARGs in sediments. The results showed that:① 162 and 79 ARGs were detected in urban (4 776 ±4 452) and rural rivers (1 043 ±632), respectively. The abundance and species of ARGs in urban rivers were higher than those in rural rivers. ② The relative abundances of sulfonamide (SAs,27 %), aminoglycoside (AGs,26 %), and multidrug (MDs,15 %) ARGs had the highest abundance in urban rivers, whereas the relative abundance of MDs ARGs was highest in rural rivers (65 %). On the whole, the complexity of ARGs in urban rivers was higher than that in rural rivers. ③ There was a significant positive correlation between SAs, AGs, MDs, tetracycline, phenicol, macrolides-lincosamids-streptogramins (MLS), β-lactams, and diaminopyrimidine ARGs in urban rivers (P &lt; 0.01); however, there was a significant negative correlation between glycopeptide ARGs and all types of ARGs (P &lt; 0.05 and P &lt; 0.01). There was a significant positive correlation between MDs and SAs ARGs in rural rivers (P &lt; 0.05), but there was a significant negative correlation between amino aminocoumarin, peptide, rifamycin, and fosfomycin ARGs (P &lt; 0.05 and P &lt; 0.01). ④ For the temporal variation in urban rivers, 162 ARGs (4 776 ±4 452) and 148 ARGs (5 673 ±5 626) were detected in December and April, respectively. For the temporal variation in rural rivers, 79 species (1 043 ±632) and 46 species (467 ±183) were detected in December and April, respectively. ⑤ RDA analysis results showed that the spatial-temporal distributions of ARGs in urban and rural rivers were different. Correlation analysis showed that the ARGs in urban rivers were significantly correlated with the number of industrial enterprises, whereas the ARGs in rural rivers were significantly correlated with the output value of animal husbandry. In general, this study identified the main influencing factors for ARGs in different rivers and provided data support for ARGs risk management in different rivers.

Evaluation of the Pacific oyster marine aquaculture suitability in Shandong, China based on GIS and remote sensing

The Pacific oyster (Crassostrea gigas) is a marine aquaculture species with rapid production growth in recent years. China accounts for nearly 90% of global production by 2021, especially in Shandong province. Evaluating suitability is crucial for ensuring the sustainable growth of Pacific oyster marine aquaculture and achieving a blue transition. This study developed a suitability evaluation model for Pacific oyster marine aquaculture using a Geographic Information System (GIS), Maximum Entropy (MaxEnt) model, remote sensing, and reanalysis data. A literature review and Analytic Hierarchy Process (AHP) were used to establish an evaluation model encompassing water quality, hydrology, climate and meteorology, and socioeconomic factors. The results showed that within a 20 km range of the Shandong coast, 49% of the area was highly suitable, 51% was moderately suitable, and the overall annual high score proportion (HSP) fluctuated around 50%, with higher suitability observed in the spring and autumn. The inner bays of the coastal areas (Laizhou, Rongcheng, Jimo) exhibited high suitability (HSP over 80%); in contrast, the offshore areas (Changdao, Rushan) farther from the coast had lower suitability and showed significant monthly variations. The result was consistent with the spatial distribution and temporal variation of Shandong’s existing Pacific oyster marine aquaculture areas. The study also found that El Niño significantly impacts Rongcheng, Rushan, and Jimo during summer. We predicted an overall increase of suitability in the Shandong offshore areas under future climate change scenarios, with a more significant increase of suitability in the north. El Niño-Southern Oscillation (ENSO) influenced the concentration of parameters such as chlorophyll-a (Chl-a) and total suspended sediment (TSS) in the coastal waters through its impact on precipitation (Pr), resulting in suitability fluctuations.

Spatial and Temporal Variations in Land Surface Temperature as an Indicator of Learning Comfort Level in Banten Province

Increasing population and rapid development of built-up land can cause an increase in land surface temperature. These changes are often associated with many factors, including changes in land use, and surface parameters, economic development, and vegetation. This research aims to analyze the temperature factor as an indicator of indoor thermal comfort to support the learning process in schools in the Banten Province area. This research uses a spatial temporal approach with descriptive statistical analysis. Based on the results, show that the land surface temperature at each school location in Banten province shows conditions that are not constant. The number of schools with uncomfortable conditions varies from 2019 (89.07%), 2020 (42.65%), 2021 (45.66%), 2022 (82.66%), and 2023 (52.44%). This shows that there has been a decrease in uncomfortable conditions for studying by 36.63% in the last 5 years.

Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20 July 2021: Temporal–Spatial Variability and Implications for Radar QPE

Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20 July 2021: Temporal–Spatial Variability and Implications for Radar QPE