Solar modulation of galactic cosmic rays during 19–23 solar cycles

We make a detailed analysis of cross-correlation and time-lag between monthly data of galactic cosmic rays (GCRs) intensity and different solar activity indices (e.g., sunspot number, sunspot area, green coronal Fe line and 10.7 cm solar radio flux) during 19–23 solar cycles. GCRs time-series data from Kiel neutron monitor station and solar data from the last 50 years period, covering five solar cycles (19–23), and alternating solar polarity states (i.e., five A < 0 and four A > 0) have been investigated. We find a clear asymmetry in the cross-correlation between GCRs and solar activity indicators for both odd and even-numbered solar cycles. The time-lags between GCRs and solar parameters are found different in different solar cycles as well as in the opposite polarity states (A < 0 and A > 0) within the same solar cycle. Possible explanations of the observed results are discussed in light of modulation models, including drift effects.     

Evaluation of the intermediate-term periodicities in solar and cosmic ray activities during cycle 23

In this paper we investigate the presence and temporal evolution of galactic cosmic rays (GCRs) time-series and three solar parameters, namely the daily sunspot number, the coronal green line and the 10.7 cm solar radio flux over the period 1996–2003 by the wavelet technique. A number of short- and intermediate-term quasi-periodicities were also detected in both GCR and solar parameters. For the short-term range, we have identified quasi-periods of 16–30 days, 40–55 days, 60–70 days, 80–90 days and 80–100 days. In the case of intermediate range, the significant periods were 120–140 days, 150–170 days, 190–210 days, 240–260 days, ≈1.09 yr. and ≈1.23 yr. The wavelet power spectra show that all the above-mentioned periods are intermittent in nature and occurred in different time-series in different intervals. The result exhibits that the well-known “Rieger period” of (150–160 days) was prominent in both GCR and solar data sets during the ascending phase of cycle 23. Possible reasons behind the observed periods were discussed with the help of previous results and existing numerical models.     

Hydrochemistry and Classification of Groundwater Resources of Ishwardi Municipal Area,Pabna District,Bangladesh

The chemical property of groundwater depends largely on the mineralogical composition of the rocks through which the water has moved and the rate of movement and these characteristics of surface water depend on organic and inorganic reactions, industrial effluents, rainfall and temperature etc. The underground water tends to contain more dissolved materials than those in surface water because of their more intimate and longer contact with organic materials of soil and rock particles. The groundwater of the studied area is dominant of alkaline earth’s (Ca²⁺ and Mg²⁺) and weak acids (HCO₃ ⁻) which may be classified as Magnesium-Bicarbonate and Calcium Carbonate types. Genetically, the groundwater of the area belongs to both “Normal Chloride” “Normal Sulphate” and “Normal Carbonate” to “Super Carbonate” group. Based on EC, SAR and RC, the groundwater of the area varies from good–excellent quality for irrigation purposes with low alkali hazard and medium salinity hazard.     

Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques

An approach is presented for the evaluation of groundwater potential using remote sensing, geographic information system, geoelectrical, and multi-criteria decision analysis techniques. The approach divides the available hydrologic and hydrogeologic data into two groups, exogenous (hydrologic) and endogenous (subsurface). A case study in Salboni Block, West Bengal (India), uses six thematic layers of exogenous parameters and four thematic layers of endogenous parameters. These thematic layers and their features were assigned suitable weights which were normalized by analytic hierarchy process and eigenvector techniques. The layers were then integrated using ArcGIS software to generate two groundwater potential maps. The hydrologic parameters-based groundwater potential zone map indicated that the ‘good’ groundwater potential zone covers 27.14% of the area, the ‘moderate’ zone 45.33%, and the ‘poor’ zone 27.53%. A comparison of this map with the groundwater potential map based on subsurface parameters revealed that the hydrologic parameters-based map accurately delineates groundwater potential zones in about 59% of the area, and hence it is dependable to a certain extent. More than 80% of the study area has moderate-to-poor groundwater potential, which necessitates efficient groundwater management for long-term water security. Overall, the integrated technique is useful for the assessment of groundwater resources at a basin or sub-basin scale.     

Seismic hazard assessment for Mymensingh,Bangladesh

Mymensingh municipality lies in one of the most earthquake-prone areas of Bangladesh. The town was completely destroyed during the Great Indian Earthquake of 12 June 1897, for which the surface-wave magnitude was 8.1. In this study the 1897 Great Indian Earthquake was used as a scenario event for developing seismic microzonation maps for Mymensingh. For microzonation purposes SPT data from 87 boreholes were collected from different relevant organizations. To verify those data ten boreholes of depth up to 30 m were drilled. Intensity values obtained for different events were calibrated against attenuation laws to check applicability to the study area. Vibration characteristics at diverse points of the study area were estimated by employing the one-dimensional wave-propagation software SHAKE. SHAKE discretizes the soil profile into several layers and uses an iterative technique to represent the non-linear behavior of the soil by adjusting the material properties at each iteration step. The required input information includes depth, shear wave velocity, damping factor, and unit weight of each soil layer. The liquefaction resistance factor and the resulting liquefaction potential were estimated to quantify the severity of liquefaction. Quantification of secondary site effects and the weighting scheme for combining the various seismic hazards were heuristic, based on judgment and expert opinion.     

Spatiotemporal variation and mechanisms of temperature inversion in the Bay of Bengal and the eastern equatorial Indian Ocean

In the northern Bay of Bengal, the existence of intense temperature inversion during winter is a widely accepted phenomenon. However, occurrences of temperature inversion during other seasons and the spatial distribution within and adjacent to the Bay of Bengal are not well understood. In this study, a higher resolution spatiotemporal variation of temperature inversion and its mechanisms are examined with mixed layer heat and salt budget analysis utilizing long-term Argo（2004 to 2020） and RAMA（2...     

Socio-economic drivers of deforestation in Roghani Valley,Hindu-Raj Mountains,Northern Pakistan

Deforestation and associated ecological disturbances are the issues of global concern.Researchers have investigated a number of driving forces which accelerate the rate of deforestation at local and regional levels.These include poverty,population growth,market demand and prices,political instability,agricultural expansion and changes in property right and ownership regimes.This paper seeks to explore the impacts of population growth,changing tenure system and other socioeconomic factors on the forest cover of Roghani Valley,located in Hindu Raj Mountains,Northern Pakistan.The present study is mainly based on information collected through participatory observation,selfadministered interviews and questionnaire survey.Geographical Information System（GIS） database is also used for mapping and quantification.The results reveal that in the past three to four decades the study area has been subjected to severe deforestation and about half of the forest area has been converted into barren land.Thus,the area under natural forests decreased from 2099 to 1444 hectares in four decades.This large-scale deforestation is attributed to both proximate and under lying causes particularly traditional land tenure system and demographic development.Consequently,forest resources have been degraded and a number of plant species have disappeared from the forests of the study area while several others are in the process of disappearance.     

Partitioning of dioxins (PCDDs/Fs) in ambient air at urban residential locations

In this work, 17-polychlorinated dibenzo-p-dioxin/furan (PCDD/Fs) isomers were measured in ambient air at four urban sites in Seoul, Korea (from February to June 2009). The concentrations of their summed values (ΣPCDD/Fs) across all four sites ranged from 1,947 (271 WHO₀₅ TEQ) (Jong Ro) to 2,600 (349 WHO₀₅ TEQ) fg/m³ (Yang Jae) with a mean of 2,125 (± 317) fg/m³ (292 WHO₀₅ TEQ fg/m³). The sum values for the two isomer groups of ΣPCDD and ΣPCDF were 527 (30 WHO₀₅ TEQ) and 1,598 (263 WHO₀₅ TEQ) fg/m³, respectively. The concentration profile of individual species was dominated by the 2,3,4,7,8-PeCDF isomer, which contributed approximately 36 % of the ΣPCDD/Fs value. The observed temporal trends in PCDD/F concentrations were characterized by relative enhancement in the winter and spring. The relative contribution of different sources, when assessed by principal component analysis, is explained by the dominance of vehicular emissions along with coal (or gas) burning as the key source of ambient PCDD/Fs in the residential areas studied.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Effects of over-consolidation history on flow instability of clean and silty sands

Acta Geotechnica - This article presents an experimental program under the constant volume condition to investigate the influence of over-consolidation on flow instability of clean and silty sand...