Journal of Environmental Engineering and Landscape Management

The seasonal change of water quality parameters and ecological condition of some surface water bodies in the Nemunas River basin

Jolita Bradulienė — 2024-10-04

The surface water quality analysis is very important in order to identify potential sources of contamination. The pollution of surface water can occur because of unauthorized discharge of a variety of materials or pollutants, and cultivated fields from which migratory pollutants are carried into the water bodies by melting snow. The current paper presents the results of quality indicators’ analysis (oxygen saturation (dissolved oxygen) (mg O₂/l); an active water reaction, pH; suspended solids (mg/l); biochemical oxygen demand BOD₇ (mg O₂/l); phosphate (mgP/l); nitrite (mgN/l); nitrate (mgN/l); ammonium (mgN/l); total phosphorus (mgP/l); total nitrogen (mgN/l); colour (mg/l Pt)) of some surface water bodies (the Dubysa, Reizgupis, Vilkupis, Kriokle Rivers and Prabaudos pond) in the Nemunas River basin. The research demonstrated that the majority of non-compliances and exceedances with values and the maximum allowable concentrations stated in the hygiene norms can be found in the Reizgupis River. According to the analyzed surface water quality indicators, the ecological conditions of the surface water bodies were determined.

Evolution characteristics of landscape ecological risk patterns in Shangluo City in the Qinling Mountains, China

Shu Fang — 2024-10-22

Landscape ecological risk assessment (LERA) is the basis of regional landscape pattern optimization, and a tool that can help achieve a win-win situation between regional development and ecological protection. The landscape ecological risk (LER) of the southern end of the Qinling Mountains, China exhibited an increasing trend after the year 2000, but the degree of increase and the spatial and temporal dynamics were not clear, limiting the formulation and implementation of landscape optimization measures in the area. Here, we constructed a landscape pattern risk index ERI by combining data on landscape disturbance and landscape vulnerability from land use information for Shangluo City for years 2000, 2005, 2010, 2015, and 2020; then, we calculated a LER level and its spatial and temporal dynamics for Shangluo City for years 2000 to 2020. Moran’s I and LISA indices were used to characterize the spatial correlation of ERI in Shangluo City. We found that Shangluo had a large proportion of medium-risk areas, and its LER shifted from medium-high, high in year 2000 to medium risk, medium-low and low risk in year 2020, and LER of Shangluo was clustered in space but the degree of clustering decreased in the past 20 years. We conclude that the development strategy of Shangluo should depend on providing a sustainably-developed environment.

Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods

Deepak Kumar Raj — 2024-10-30

This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.

Assessing of monthly surface water changes impact on thermal human discomfort in Baghdad

Jamal S. Abd Al Rukabie — 2024-11-06

In urban areas, surface water bodies play an important role in mitigating thermal discomfort, which is mainly caused by increasing air temperatures. Based on daily temperature and relative humidity data recorded by the Baghdad weather station for the two years 2018 and 2021, the monthly human discomfort index was calculated and then combined with monthly surface water areas extracted by a modified normalized difference water index using Sentinel-2A satellite imagery for the same period. The results show that the winter and most spring months of these years have no discomfort, and the summer months (July and August) in 2021 have the highest discomfort with severe thermal stress due to the large deficit in rainfall events. The monthly relationship between urban water surfaces and the level of the discomfort index was also studied, which was non-linear and followed the exponential decay function. This means that as the amount of surface water increased, the levels of the discomfort index decreased exponentially until no discomfort conditions existed.

Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam

Tuyet Nam Thi Nguyen — 2024-11-13

This study aims to predict fine particulate matter (PM2.5) pollution in Ho Chi Minh City, Vietnam, using autoregressive integrated moving average (ARIMA), linear regression (LR), random forest (RF), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and convolutional neural network (CNN) combining Bi-LSTM (CNN+Bi-LSTM). Two experiments were set up: the first one used data from 2018–2020 and 2021 as training and test data, respectively. Data from 2018–2021 and 2022 were used as training and test data for the second experiment, respectively. Consequently, ARIMA showed the worst performance, while CNN+Bi-LSTM achieved the best accuracy, with an R² of 0.70 and MAE, MSE, RMSE, and MAPE of 5.37, 65.4, 8.08 µg/m³, and 29%, respectively. Additionally, predicted air quality indexes (AQIs) of PM2.5 were matched the observed ones up to 96%, reflecting the application of predicted concentrations for AQI computation. Our study highlights the effectiveness of machine learning model in monitoring of air pollution.

Analysing the relationship between spatial configuration and land use of the Ordu city with the space syntax approach

Murat Yesil — 2024-11-14

Cities, which are a product of human societies and the construction of civilization, are places where individuals spend a significant part of their daily lives. In this respect, the way urban space is organized and the qualities it possesses deeply affect urban life and usage practices. In this context, the research aims to reveal the relationship between spatial configuration and land uses in the region defined as the core of Ordu city centre with analytical methods. The main method followed in the study is based on the space syntax approach, which quantitatively reveals the spatial structure that constitutes the city. As a result of the study, a consistent relationship was found between the findings obtained from axial analysis and the uses in the space. The zone with the highest intelligibility is Zone 6, which is characterized by low-density commercial areas. The zone with the highest synergy value is Zone 7, which includes urban residential areas and low-density commercial areas.

Spatio-temporal evolution and driving factors of landscape pattern in minority villages: a case study of Zahan Village in Hainan Province

Shan Zhang — 2024-11-18

Ethnic minority settlements, as an important medium for the transmission of ethnic cultures, are also a key resource for accelerating the development of ethnic minorities and the regions where they are located. Currently, research on landscape patterns focuses on traditional villages and ancient villages, whereas there is a relative lack of discussion on ethnic minority settlements. This study focuses on the multi-ethnic Zahan Village in Hainan Province, adopting the analysis methods of landscape pattern index and land-use transfer matrix, based on the theoretical framework of landscape ecology, to systematically analyze the spatial and temporal characteristics of the landscape pattern of the village and its patterns between 2007 and 2022, and to qualitatively analyze the influencing factors of its landscape changes from two dimensions, namely, humanities and nature. Research findings: (1) As the dominant landscape type, the proportion of woodland (although decreasing year by year) still exceeds 80%, whereas other land types, such as watersheds and grasslands, are gradually transformed into construction land and arable land, whose increment is significant. (2) During the study period, Throughout the study period, the landscape homogeneity of Zahan Village became better and better, the landscape types tended to be richer, and the spatial heterogeneity of the landscape also increased. (3) The area of woodland landscapes shows a decreasing trend from year to year, whereas construction land and arable land show an overall increasing trend, and the area of watersheds and meadows also decreases slightly. (4) The village landscape is mainly spatially “clustered,” concentrated in the center and southern part of the village, with a few “dots” distributed in the east and northwest, and the overall trend is spreading from the center to the periphery. (5) The evolution of village landscapes is influenced by a combination of human factors, including demographic, economic, and policy factors, as well as natural geographic factors, such as topography, climate change, and precipitation. The study provides theoretical support and practical guidance for the sustainable development of Zahan Village, as well as valuable experience and inspiration for the optimal development of other minority villages.

Spatial distribution and its driving forces analysis of soil organic matter in semi-arid grassland open-pit mining areas

Zhenhua Wu — 2024-11-21

Studying the spatial distribution of soil organic matter (SOM) and exploring its driving factors in semi-arid grassland open-pit coal mining areas is crucial for sustaining ecological development and security. Currently, research on SOM in mining areas lacks large-scale investigation, sampling, spatial distribution, and driving force research for semi-arid grassland open-pit coal mining areas, and it is unable to comprehensively grasp the distribution characteristics and driving force of SOM in open-pit coal mines. In view of this, this study took the Shengli Coal Field in Xilinhot City, the hinterland of Xilingol Grassland, as an example to research the spatial distribution and driving forces of SOM in the semi-arid grassland open-pit coal mining area. The results show that: (1) Areas with high SOM content were mainly distributed in the north of open-pit germanium mine, west No. 2 open-pit mine, and No. 1 open-pit mine. Areas with low SOM content were mainly distributed on the east and southeast sides of the city. From the spatial distribution perspective, mining has a certain impact on SOM in the study area. (2) Natural factors have a higher impact on SOM changes than human factors. The order of influence degree of each factor on the spatial distribution of SOM is NDVI > Water > Agriculture > Mine > Town > Industry. The sources of influence on SOM in the research area are relatively complex. (3) The interaction between two factors presents two relationships: nonlinear enhancement and dual-factor enhancement. A single factor is lower than the interaction between various factors. In the interaction between factors, the explanation rate of interaction between Town, Agriculture, Mine, NDVI, Water, and all other factors is above 0.85. This study has important practical significance for soil management in mining areas, ecological restoration, and planning of national land space, etc.