Estimating the Precipitation Amount at Regional Scale Using a New Tool, Climate Analyzer

Modeling the Greenhouse Gases Data Series in Europe during 1990-2021

Nowadays, climate change and atmospheric pollution are two of humanity's most significant challenges. Greenhouse gases (GHGs) are responsible for climate change, and they create effects that are mostly irreversible. Therefore, monitoring and reducing such emissions are compulsory for the preservation of the environment for future generations. The European Union took action in this direction. The article presents the evolution of the total GHGs trend, from 1990 to 2021, in the EU countries and their associates. Trend analysis and grouping of the countries using different clustering techniques are performed. The analysis of the existence of greenhouse gases (GHGs) series' trend, in 30 countries from Europe, showed that the GHG emissions decreased from 1990 to 2021 in only 17 countries. The annual series, built using the data reported by each country each year, does not present a specific trend. After grouping the countries in clusters by k-means and hierarchical clustering, the representative series for the annual recorded values in the 30 studied countries, called Regional series (RegS), is built using series selected from the cluster with the highest number of elements. The same algorithm provides the Representative Temporal series (TempS), which selects specific years after clustering the annual GHG series.

Assessing Atmospheric Pollution and Its Impact on the Human Health

In recent decades, atmospheric pollution has become a major risk for public health and ecosystems [...]

Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania

Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account of an event in summer 2020, when the lake surface water decreased significantly, this study aims to identify the variation of the Nuntasi-Tuzla Lake surface water over a long-term period in correlation with human intervention and climate change. To this end, it provides an analysis in the period 1965–2021 via hydrological drought indices and data mining classification. The latter approach is based on several water indices derived from Landsat TM/ETM+/OLI and MODIS full-time series datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (NDVI), Modified NDWI (MNDWI), Weighted Normalized Difference Water Index (WNDWI), and Water Ratio Index (WRI). The experimental results indicate that the proposed classification methods can extract relevant features from waterbodies using remote sensing imagery with a high accuracy. Moreover, the study shows a similarity in the evolution of surface water cover identified with the data mining classification and the drought periods detected in the flow data series for the Nuntasi and Sacele Rivers that supply the Nuntasi-Tuzla Lake. Overall, the results of our investigation show that human intervention and hydrological drought had an extensive impact on the long-term changes in surface water of the Nuntasi-Tuzla Lake.

Assessing the Water Pollution of the Brahmaputra River Using Water Quality Indexes

Water quality is continuously affected by anthropogenic and environmental conditions. A significant issue of the Indian rivers is the massive water pollution, leading to the spreading of different diseases due to its daily use. Therefore, this study investigates three aspects. The first one is testing the hypothesis of the existence of a monotonic trend of the series of eight water parameters of the Brahmaputra River recorded for 17 years at ten hydrological stations. When this hypothesis was rejected, a loess trend was fitted. The second aspect is to assess the water quality using three indicators (WQI)-CCME WQI, British Colombia, and a weighted index. The third aspect is to group the years and the stations in clusters used to determine the regional (spatial) and temporal trend of the WQI series, utilizing a new algorithm. A statistical analysis does not reject the hypothesis of a monotonic trend presence for the spatially distributed data but not for the temporal ones. Hierarchical clustering based on the computed WQIs detected two clusters for the spatially distributed data and two for the temporal-distributed data. The procedure proposed for determining the WQI temporal and regional evolution provided good results in terms of mean absolute error, root mean squared error (RMSE), and mean absolute percentage error (MAPE).