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

2024-10-04T18:28:16+03:00

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

2024-10-22T18:28:37+03:00

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

2024-10-30T18:28:43+02:00

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

2024-11-06T18:28:52+02:00

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

2024-11-13T18:28:58+02:00

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.