Spatial–Temporal Distribution of the Euphausiid Euphausia pacifica and Fish Schools in the Coastal Southwestern East Sea

The spatial and temporal distribution of euphausiid Euphausia pacifica and fish schools were observed along acoustic transects in the coastal southwestern East Sea. Two-frequency (38- and 120-kHz) acoustic backscatter data were examined from April to July 2010. A dB identification window (SV120–38) and school detection algorithm identified E. pacifica and fish schools in the acoustic backscatter, respectively. The E. pacifica was regularly observed in middle of southern waters, where phytoplankton was abundant during spring, and irregularly during summer, when phytoplankton was homogeneously distributed. Using the distorted-wave Born approximation model, the acoustic density of E. pacifica calculated was higher in spring (April: 75.9 mg m−2, May: 85.3 mg m−2) than in summer (June: 71.4 mg m−2, July: 54.1 mg m−2). The fish schools in the acoustic data tended to significantly increase from spring to summer. Although major fish species, such as anchovies and herring, fed on copepods and euphausiids in the survey area, the temporal and spatial distribution of E. pacifica was weakly correlated with the distribution of the fish schools. These findings aid in our understanding of the temporal and spatial distribution dynamics of euphausiids and fish schools in the food web of the coastal southwestern East Sea.

Sentinel surveillance for influenza A viruses in Lahore District Pakistan in flu season 2015–2016

Background Influenza A virus (IAV) remains an important global public health threat with limited epidemiological information available from low-and-middle-income countries. The major objective of this study was to describe the proportions, temporal and spatial distribution, and demographic and clinical characteristics of IAV positive patients with influenza like illness (ILI) and severe acute respiratory illness (SARI) in Lahore, Pakistan. Methods Prospective surveillance was established in a sentinel hospital from October 2015 to May 2016. All eligible outpatients and inpatients with ILI or SARI were enrolled in the study. Nasal and/or throat swabs were collected along with clinico-epidemiological data. Samples were tested by real-time RT-PCR (rRT-PCR) to identify IAV and subtype. The descriptive analysis of data was done in R software. Results Out of 311 enrolled patients, 284 (91.3%) were ILI and 27 (8.7%) were SARI cases. A distinct peak of ILI and SARI activity was observed in February. Fifty individuals (16%) were positive for IAV with peak positivity observed in December. Of 50 IAV, 15 were seasonal H3N2, 14 were H1N1pdm09 and 21 were unable to be typed. The majority of IAV positive cases (98%) presented with current or history of fever, 88% reported cough and 82% reported sore throat. The most common comorbidities in IAV positive cases were hepatitis C (4%), obesity (4%) and tuberculosis (6%). The highest incidence of patients reporting to the hospital was seen three days post symptoms onset (66/311) with 14 of these (14/66) positive for IAV. Conclusion Distinct trends of ILI, SARI and IAV positive cases were observed which can be used to inform public health interventions (vaccinations, hand and respiratory hygiene) at appropriate times among high-risk groups. We suggest sampling from both ILI and SARI patients in routine surveillance as recommended by WHO.

Numerical Modeling the Flood and Pollutant Transport Processes in Residential Areas with Different Land Use Types

Large-scale flooding causes widespread disaster, and harmful pollutant concentration in water following flood affects public safety and the environment. In this study, a numerical model for solving the 2D shallow water equations and the solute transport equation is proposed to simulate overland flood and pollutant transport caused by floods. The present model is verified by comparing the predictions with the analytical solutions and simulation results; sufficiently high computational accuracy is achieved. The model is also used to simulate flood inundation and pollution spread in the area of Hun and Taizi Lane (HTL) in China due to river dike breaches; the results show that the coupling model has excellent performance for simulating the flooding process and the temporal and spatial distribution of pollutants in urban or rural areas. We use remote sensing techniques to acquire the land coverage in the area of HTL based on Landsat TM satellites. The impacts of changed land use on mitigation of flooding waves and pollutant spread are investigated; the results indicate that the land cover changes have an obvious influence on the evolution process of flood waves and pollutant transport in the study areas, where the transport of pollutants is very dynamic during flood inundation in HTL area. Furthermore, the motion of pollutants considering anisotropic diffusion is more reasonable than that due to isotropic dispersion in simulating pollutant transport associated with the flood in urban or farmland environments.

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo’s overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

Integrating Stable Isotopes with Mean Residence Time Estimation to Characterize Groundwater Circulation in a Metamorphic Geothermal Field in Yilan, Taiwan

This paper presents a water circulation model by combing oxygen and hydrogen stable isotopes and mean residence time (MRT) estimation in a high-temperature metamorphic geothermal field, Tuchen, in Yilan, Taiwan. A total of 18 months of oxygen and hydrogen stable isotopes of surface water and thermal water show the same variation pattern, heavier values in summer and lighter values in the rest of the year. A shift of δ18O with a relative constant δD indicates the slow fluid–rock interaction process in the study area. Two adjacent watersheds, the Tianguer River and Duowang River, exhibit different isotopic values and imply different recharge altitudes. The seasonal variation enabled us to use stable isotope to estimate mean residence time of groundwater in the study area. Two wells, 160 m and 2200 m deep, were used to estimate mean residence time of the groundwater. Deep circulation recharges from higher elevations, with lighter isotopic values, 5.9‰ and 64‰ of δ18O and δD, and a longer mean residence time, 1148 days, while the shallow circulation comes from another source with heavier values, 5.7‰ and 54.4‰ of δ18O and δD, and a shorter mean residence time, 150 days. A two-circulation model was established based on temporal and spatial distribution characteristics of stable isotopes and the assistance of MRT. This study demonstrates the usefulness of the combined usage for further understanding water circulation of other various temperatures of metamorphic geothermalfields.

Principal component analysis of rainwater composition at BAPMoN stations in India

Data from Indian BAPMoN stations were analyzed using the Principal Component Analysis (PCA) by examining broadly the temporal and spatial distribution characteristics of the ions, from mineral and gaseous sources, observed in rainwater samples collected over the Indian BAPMoN stations over along period (1976-87), The results show that the pH of rainwater can be generally explained In terms of the concentration of SO.-2 , NO3 -1, CI-l, Ca+2 and Na+1 ion~, However, other mechanisms could determine the overall nature of the Interactions, These mechanisms have become more clear by performing principal component analysis.

Trend Analysis and Temporal and Spatial Distribution of Wet Bulb Globe Temperature as a Heat Stress Index in Iran during the Summer Season over a 30-year Period

Introduction: Global warming is one of the most important environmental problems that have raised researchers’ attention. The present study aimed to analyze heat stress trends using the Wet Bulb Globe Temperature (WBGT) index in the country of Iran during the summer over a 30-year period. Materials and Methods: Daily summertime statistical data regarding mean temperature and mean relative humidity, taken from 40 synoptic meteorological stations across Iran during a 30-year period were obtained from the Iranian National Meteorological Department. The De Martonne climate classification system was used to categorize various climate regions of Iran. The WBGT index was calculated using the formula given by the Australian Bureau of Meteorology. The Mann-Kendall statistical test and the Sen's slope estimator were used to analyze the trends of the WBGT index. Results: The WBGT index had an upward trend during the three months of June, July, and August in 71.42%, 57.14%, and 66.66% of all stations and this trend was statistically significant in 53.32%, 50%, and 42.85% of those stations, respectively. Moreover, throughout the summer, 45% of the WBGT index measurements were in the medium range (18-23°C), 37.5% were in the high range (23-28°C), and 17.5% were in the very high range (> 28°C). Conclusion: The WBGT index followed an upward trend during the summer, especially in semi-arid regions of Iran. Considering the phenomenon of global warming, it is essential to monitor, plan ahead, and take necessary precaution measures for sensitive populations who are at high risk areas of the country.

Occurrence of dry and wet weeks over Maharashtra

Based on the daily rainfall data of the past 90 years (1901-90), the initial and conditional probabilities of a wet week and the probabilities of 2 and 3 consecutive wet weeks have been computed for all the districts of Maharashtra during the southwest monsoon season by using Markov Chain model. A temporal and spatial distribution of probabilities of wet weeks have been studied in detail. Most of the districts show the highest probability of wet weeks during July. A few number of the districts show the second highest probability during August. The western and northeastern parts of the state show 10-16 wet weeks with high probability. The high rainfall districts along the west coast show high wet week probabilities during most of the period of the season. A few number of the districts from moderate rainfall zone, show high probability of a wet week during, July and August. A persistency in rainfall is noticed in only extreme western parts of the state. The east-west variation along 19° N shows 'L' shaped pattern for the high probability wet weeks. While, the north -south variation of the wet weeks with high probability shows a sinusoidal curve from north to south.

Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.