VNU Journal of Science: Earth and Environmental Sciences

The world mathematicians given many method to adjust the free network, in which the confirmation that the first norm of the solution vectors must minimizing to be the standard for finding the solution in a multitude of solutions. This also conform with the weight transformation process in the deformation model to find the solution for the most probable model, developed by Adam Chrzanowski. The geodetic base point at hydropower plants are used as benchmarks to assess the displacement of test points are attached on the dam. This article presents the iterative weight transformation technique of  the problem handle the free geodetic network at Tuyen Quang hydropower. The results showed that the largest displacement value was 2.2 mm / year and equivalent to the actual measurement error. This calculation method provides more useful information about the displacement model of geodetic base points, helping to plan a large-scale project safety assurance.      

Abstract: Deformation is a process that is formed from numerous subjective and objective reasons, caused by both environment and climate change. The continuous and abnormal behavior of the environment along with the internal stress generated in the building itself leads to destructive deformation. Deformation analysis, therefore, requires a systematic model for identifying and predicting impact on the safety of the building. This paper discusses the deformation models, estimation methods and evaluate the deformation of the buildings by two different reasons in Van Quan urban area, Hanoi City. Bothparameter modeling (Kalman filter application for known structures), and non-parametric modeling (application of time series for unknown soft ground) are applied with the theory of system identification of Heunecke and Welsch. Keywords: Deformation, parametric and non-parametric modeling, Kalman filter, time series. References Tài liệu tham khảo [1] Grewal, Mohinder S, Angus P. Andrews. Kalman filtering : theory and practice using MATLAB. Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2008. [2] George E.P. Box, Gwilym M. Jenkins, Gregory C. Reinsel. 4th ed. Time series analysis: forecasting and control. Published by John Wiley & Sons, Inc., Hoboken, New Jersey, Canada, 2008. [3] Walter M.Welsch, Otto Heunecke, Models and Terminology for the Analysis of Geodetic Monitoring Observations,Official Report of the Ad-Hoc Committee of FIG Working Group 6.1,2001. [4] Phan Văn Hiến (Chủ biên), Đinh Xuân Vinh, Phạm Quốc Khánh, Tạ Thanh Loan, Lưu Anh Tuấn, Lý thuyết sai số và Bình sai trắc địa. Nhà xuất bản Xây dựng,2017. [5] Cankut D. Ince and Muhammed Sahin, Real-time deformation monitoring with GPS and Kalman Filter, Istanbul Technical University, Faculty of Civil Engineering, Department of Geodesy and Photogrammetry, 80620 Maslak, Istanbul, Turkey, [6] Antti Lange, Optimal Kalman Filtering for ultra-reliable Tracking, Proceedings of the Symposium “Atmospheric Remote Sensing using Satellite Navigation Systems”Matera, Italy, 13-15 October 2003. [7] Lihua Li, Heiner Kuhlmann, Detection of deformations and outliers in real-time GPS measurements by Kalman filter model with shaping filter, 13th FIG, 4th IAC, [8] Yam Khoon Tor. Application of Kalman Filter in Real-Time Deformation Monitoring using Surveying Robot. Surveying - Civil Engineering Research, [9] Phan Văn Hiến, Đinh Xuân Vinh, Ứng dụng lọc Kalman trong phân tích biến dạng.Tạp chí Khoa học Kỹ thuật Mỏ - Địa chất, số 31 (7-2010). Đinh Xuân Vinh (Chủ biên), Phan Văn Hiến, Nguyễn Bá Dũng, Lý thuyết và phương pháp phân tích biến dạng. Nhà xuất bản Tài nguyên-Môi trường và Bản đồ Việt Nam, Hà Nội, Fernando Sansò and Antonio J. Gil, Geodetic Deformation Monitoring: From Geophysical to Engineering Roles.IAG Symposium, Vol. 131, Jaén, Spain. Springer, 2005. Hoàng Trọng, Chu Nguyễn Mộng Ngọc. Thống kê ứng dụng trong kinh tế xã hội. Nhà xuất bản Thống kê, Hà Nội, Nguyễn Cao Văn, Trần Thái Ninh, Giáo trình lý thuyết xác suất thống kê toán. Nhà xuất bản Khoa học và Kỹ thuật, Hà Nội, 1996. Lê Khánh Luận, Nguyễn Thanh Sơn, Lý thuyết xác suất thống kê. Nhà xuất bản Đại học Quốc gia Thành phố Hồ Chí Minh, 2013. Søren Bisgaard, Murat Kulahci, Time series analysis and forecasting by example. Published by John Wiley & Sons, Inc., Hoboken, New Jersey, Canada, Đinh Xuân Vinh (Chủ nhiệm), Lê Thị Nhung, Nguyễn Văn Quang.Nghiên cứu ứng dụng phương pháp Chuỗi thời gian (Time Series) xây dựng Mô hình toán học dự báo chuyển dịch của các điểm khống chế trắc địa,Báo cáo đề tài Nghiên cứu khoa học cấp cơ sởTrường Đại học Tài nguyên và Môi trường Hà Nội,mã số 13.01.14.O.02, 2014.    

The area of Van Quan, Hanoi before 2004 was the rice field. Nearby, Ha Dinh water plant has  well-drilled underground water for residential activities. Van Quan's new urban area after being formed has detected many subsidences. The objective of this study is to assess the main causes of the subsidence of the houses, based on groundwater and soil. This paper applied the regression method to study the effect of soil and groundwater on the residential constructions in Van Quan urban area, Hanoi. Subsidence monitoring was carried out for 4 consecutive years, from 2005 to 2009, including over 500 subsidence monitoring points with high-precision Ni007 and INVAR gauges. A groundwater observation well is 30 meters deep at the site of the settlement. The results show a small effect of groundwater on subsidence. The characteristics of the young sediment area and the soil consolidation process are the main causes leading to serious subsidence in residential constructions in Van Quan urban area. This paper provides a different perspective on the impact of groundwater on the subsidence of residential structures within approximately 100 ha.

Although spent mushroom substrate (SMS) is one of the factors causing pollution for mushroom production area, SMS is a nutrient-rich organic material which can be used for compost production, especially when it combines with other agricultural by-products. The analysis results show that pH (7.22-7.87) and moisture (60.20-73.28%.) of compost products made from all formulas are suitable for many types of crop, particularly, organic matter content is very high in formula 1 (CT1) with 72.20%, formula 3 (CT3) with 61.94% and the lowest value is 27.62%at formula 5 (CT5). Total nitrogen content reaches the highest value at the CT1 (0.58%), following is formula 2 (CT2)at 0.55%, and the CT5 has the lowest content with 0.25%; Total phosphorus content, which is relatively low, merely obtains 0.35% in control formula, 0.22% at the CT2 and the lowest point is 0.15% at the CT5; Total potassium contents are quite equal, in which, the CT5 has the highest value with 0.80% and formula 4 has the lowest value with 0.46%. The research also indicates thatnutrient norms of the products from mixed formulas with stalks and leaves of maize, peanut and kudzu (CT1, CT2 and CT3) are better than those from the remain formulas. However, with the pak choi planting experiments,only composts from the CT1 and the CT3 make optimal conditions for the growth of plants while pak choi in experiment with the product from the CT2 is less developed than plant in experiments with other products. Thus, the combination of maize stalks and leaves, and SMS in compost production gives huge potential in enhancement of product quality after treament, as well as reduces the risk of pollution from agricultural by-products.

Lãnh thổ Móng Cái có điều kiện tự nhiên đa dạng và phân hóa phức tạp, bao gồm cả lãnh thổ trên đất liền, trên biển, có cửa khẩu tạo nên tính đặc thù trong khai thác, sử dụng tài nguyên phục vụ phát triển một nền kinh tế tổng hợp. Tuy nhiên, Móng Cái cũng đang đứng trước nhiều thách thức trong tiến trình phát triển, đặc biệt là tìm kiếm các giải pháp nhằm phát huy được lợi thế tiềm năng lãnh thổ, đồng thời giảm thiểu những tác động bất lợi tới môi trường hướng tới mục tiêu phát triển bền vững. Vấn đề này có thể được giải quyết một cách hiệu quả hơn khi dựa trên nghiên cứu tổng hợp các điều kiện địa lý, bao gồm địa lý tự nhiên (chú trọng nghiên cứu cảnh quan), địa lý kinh tế-xã hội và địa lý môi trường. Đây là những cơ sở địa lý cho xác định khung không gian phát triển kinh tế gắn với sử dụng hợp lý tài nguyên và bảo vệ môi trường của thành phố Móng Cái.Từ khóa: Xác định không gian, phát triển kinh tế, bảo vệ môi trường, Móng Cái.

Located in southern Vietnam, the Southern plain is one of the largest in Asia. Within the coastal area, this study has indicated that there are two plains forming by different hydrodynamic mechanisms: the river dominated Mekong Delta plain and the tidal dominated plain of the Ca Mau peninsula. Studying lithofacies based on: (i) sedimentary parameters indicating environment of 29 boreholes in tidal flat and coastal plains, hundreds of surveyed surface sediment stations; (ii) stratigraphy seismic characteristics of the 21 seismic sections; and (iii) absolute age data, evolutionary history of late Pleistocene - Holocene sediments in the Southern plain and the relationship between the Mekong Delta and the tidal plain of the Ca Mau peninsula in the middle Holocene - late be clarified. Both plains are characterized by 3 lithofacies complexes corresponding to 3 phases of sea-level change: (i) lowstand alluvial facies complex (arLST Q13b); (ii) coastal facies complex (amtTST Q21-2) and shallow marine-lagoon greenish-gray clay facies (mtTST Q21-2); (iii) the phase of the middle-late Holocene (Q22-3 HST) has a differentiation between the two plains. The Me Kong delta is characterized by three deltaic facies complexes: (1) the late middle-late Holocene buried submarine deltaic facies complex (amh1Q22-3); (2) late Holocene deltaic plain facies complex (amh2Q23) and modern submarine deltaic facies complex (amh3Q23). The tidal plain of Ca Mau peninsula is characterized by a complex of sandy bars, tidal plains and tidal channels. In the regressive process, four periods of relative sea-level stopped, creating three ancient shoreline (5ka BP, 2.5ka BP; and 1 ka BP). The delta plain is marked by deltaic lobes turning to the southeast sea, while the Ca Mau plain characterized by the sand bars that tend to change direction from the east (2.5 ka BP) to the southeast (0.5ka BP and 0.2ka BP).    

Our preliminary study on plant diversity in Phia Oac - Phia Den Nature Reserve identified 1199 species, belonging to 611 genera, 177 families and five phylums. Among them, Magnoliophyta is the most species phylum with 939 species (accounting for 78.31% of the total species), belonging to 520 genera (85.11% ), and 144 families (81.36%). Nine dominant families contain 371 species (30.94%), belonging to 167 genera (27.33%). The main life form of plants in surveyed area is Phanerophytes (Ph) with 644 species (53.71% of total species). Plants are mainly distributed in the primary forest ecosystem of Phia Oac - Phia Den Nature Reserve (575 species or 47.79%). There are 1060 useful plant species with medicinal plants and timber trees making up the largest proportion of 40.37% and 19.77%, respectively.

The objective of the study is to assess changes of FVC in some urban and sub-urbanareas of Hanoi city 2007 to 2015 based on a two endmember spectral mixture analysis (SMA) modelusing multi-spectral and multi-temporal LANDSAT TM and OLI images. FVC was estimated for theyears of 2007 and 2015 by means of two endmember SMA based on NDVI, the assessment of FVCchanges was finally carried out. The study results show that: FVC was decreased with the total area of699.8 km2, accounting for 75.5% of total area, decreased by 87.5 km2 per year in Soc Son’s south,Dong Anh’s east, Gia Lam’s east and Thanh Tri’s west; some areas had medium and weak decreaserate such as Cau Giay, North and South -Tu Liem and Soc Son’s west; total area of almost unchangein FVC was 184.5 km2, accounting for 19.9% , occurring mainly in Ba Dinh, Dong Da, Hoan Kiem;only 44.9 km2 was increased, accounting for 4.9% of total area, only 5.6 km2 per year, mainlyconcentrated in the district of Hoang Mai, noth-eastern Soc Son, Dong Anh’s south.Keywords LANDSAT images; fractional vegetation cover change, Ha Noi city References [1] Hoffmann, W. A., & Jackson, R. (2000). Vegetation-climate feedbacks in the conversion of tropical savanna to grassland. Journal of Climate, 13, 1593–1602.[2] Ward, R. C., & Robinson, M. (2000). Principles of Hydrology (4th edition). McGraw hill. pp 450. [3] Gutman, G.; Ignatov, A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models, International Journal of Remote Sensing 1998, 19 (8), 1533-1543.[4] Zeng, X., Dickinson, R. E., Walker, A., & Shaikh, M. (2000). Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. Journal of Applied Meteorology, 39, 826–839.[5] Avissar, R; Pielke, R. A. A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology. Monthly Weather Review 1989, 117, 2113-2136.[6] Trimble, S. W. Geomorphic effects of vegetation cover and management: some time and space considerations in prediction of erosion and sediment yield, in Vegetation and Erosion, edited by J. B. Thornes, London, John Wiley & Sons, 1990, pp. 55-66.[7] Juan C. Jiménez-Muñoz, José A. Sobrino, Antonio Plaza, Luis Guanter, José Moreno and Pablo Martínez . Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area. Sensors, 2009, 9, 768–793.[8] Ying Li, Hong Wang and Xiao Bing Li. Fractional Vegetation Cover Estimation Based on an Improved Selective Endmember Spectral Mixture Model. PLoS One. 2015; 10(4): e0124608[9] Zhang Y, Li X, Chen Y. Overview of field and multi-scale remote sensing measurement approaches. Advance Earth Sci. 2003; 18 (1): 85–93.[10] Silván-Cárdenas JL, Wang L. Retrieval of subpixel Tamarix canopy cover from LANDSAT data along the Forgotten River using linear and nonlinear spectral mixture models. Remote Sens Environ. 2010; 114 (8): 1777–1790.[11] Chen F, Qiu Q, Xiong Y, Huang S. Pixel unmixing based on linear spectral mixture model: methods and comparison. Remote Sens Info. 2010; (4): 22–28[12] Xing Z, Feng Y, Yang G, Wang P, Huang W. Method of estimating vegetation coverage based on remote sensing. Remote Sens Tech Appl. 2009; 24 (6): 849–854.[13] Li M. The method of vegetation fraction estimation by remote sensing. Beijing: Chinese Academy of Sciences; 2003.[14] Li X. Quantitive retrieval of sparse vegetation cover in arid regions using hyperspectral data. Beijing: Chinese Acanemy of Forestry; 2008.[15] Small, C. (2001). Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 22, 1305–1334.[16] Theseira, M. A., Thomas, G., & Sannier, C. A. D. (2002). An evaluation of spectral mixture modeling applied to a semi-arid environment. International Journal of Remote Sensing, 23, 687–700.[17] https://www.usgs.gov/[18] Chavez, P. S. Jr. (1996). Image-Based Atmospheric Corrections – Revisited and Improved. Photogrammetric Engineering and Remote Sensing 62(9), 1025-1036. [19] Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P. and Scott, A. M. (2001). Classification and Change Detection Using LANDSAT TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment 75, 230-244.[20] Van der Meer, F. 1999. Image classification through spectral unmixing. In: Spatial Statistics for Remote Sensing, Stein, A., Van der Meer, F. & Gorte, B. (Eds.) Kluwer Academic Publishers, Dordrecht, pp. 185-193.[21] Deardorff, J. W. (1978). Efficient prediction of ground temperature and moisture with inclusion of a layer of vegetation. Journal of Geophysical Research, 83, 1889– 1903.[22] Wittich, K. P., & Hansing, O. (1995). Area-averaged vegetative cover fraction estimated from satellite data. International Journal of Biometeorology, 38, 209–215.[23] Rouse, J.W.; Haas (Jr.), R. H.; Schell, J. A.; Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. In Proc. ERTS-1 Symposium 3rd, Greenbelt, MD. 10–15 Dec. 1973. Vol. 1. NASA SP-351. NASA: Washington, DC, 1974.[24] Sobrino, J. A., Raissouni, N. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco, International Journal of Remote Sensing 2000, 21 (2), 353-366.  

Chlorophyll-a (Chl-a) has been used extensively as an essential indicator of trophic state in the assessment and monitoring of surface water quality environments. The environmental factors can influence Chl-a concentrations; thus, to determine the relationship between Chl-a concentration and factors. The research was carried out in dry season (March 2016) and wet season (September 2016) in Tri An reservoir, Dong Nai Province, Vietnam and performed by Spearman's correlation analysis and Linear regression analysis. The result showed that Chl-a varied between 12.84 and 783.51 µg/L and was quite different a cross stations in two surveys. Factor analysis and the best models revealed the association of strong physico-chemical with Chl-a concentration. The Chl-a was significantly positively correlated with Total Suspended Solids (TSS) and negative with Nitrate (NO3-) in the dry season, while in the wet season the positive relationships between Chl-a concentration and Dissolved Oxygen (DO), Temperature and a strong negatively correlated with Phosphate (PO43-) correlation were found. This relationships inferred that the nutrients brought by the influx of reservoir into the study area have contributed to control the growth and abundance of phytoplankton. Thus, the importance of environmental factors in structuring Chl-a concentration may be used to guide the conservation of the aquatic ecosystems in the reservoir.

Bài báo đưa ra một số kết quả nghiên cứu về đánh giá sự phát thải từ ống khói cụm nhà máy nhiệt điện Cẩm Phả, nhà máy nhiệt điện Mông Dương 1 và Mông Dương 2 thông qua so sánh hàm lượng các chất ô nhiễm TSP, SO2 và NOx với các quy chuẩn quốc gia và địa phương về khí thải công nghiệp (QCVN 22:2009/BTNMT, QCĐP 5:2020/QN), và ứng dụng phần mềm AERMOD VIEW 10.2.1 để mô phỏng lan truyền các chất ô nhiễm nhằm đánh giá ảnh hưởng của sự phát thải này tới chất lượng không khí xung quanh tại thành phố Cẩm Phả. Kết quả nghiên cứu cho thấy, cả 3 thông số bụi TSP, NOx và SO2 vẫn có một số giờ có nồng độ phát thải vượt quy chuẩn cho phép, với tần suất vượt chuẩn cao nhất tương ứng là 2,79%, 25,92% và 8%. Mức độ vượt chuẩn cao nhất có giá trị là 4,29 lần đối với TSP, 4,04 lần đối với NOx và 7,17 lần đối với SO2. Kết quả nghiên cứu cũng chỉ ra rằng có nơi nồng độ NO2 trung bình giờ vượt 2,7 lần so với quy chuẩn địa phương QCĐP 4:2020/QN về chất lượng môi trường không khí xung quanh. Kết quả mô phỏng lan truyền ô nhiễm đã chỉ ra khu vực trải rộng phía Tây-Tây Bắc nhà máy nhiệt điện Mông Dương 1, 2 có nồng độ khí SO2 và khí NO2 (trung bình giờ) vượt quy chuẩn cho phép hiện hành.