Ground-level ozone (O₃) is a significant source of air pollution, mainly in most urban areas across the globe. Ground-level O₃ is not emitted directly into the atmosphere. It results from photo-chemical reactions between precursors and is influenced by weather factors such as temperature. This study investigated the spatial and temporal analysis of ground-level ozone and analyzed the significant anthropogenic precursors and the weather parameters associated with ground-level ozone during daytime and nighttime at three cities in peninsular Malaysia, namely, Kuala Terengganu, Perai, and Seremban from 2016 to 2020. Secondary data were acquired from the Department of Environment (DOE), Malaysia, including hourly data of O₃ with trace gases and weather parameters. The secondary data were analyzed using temporal analysis such as descriptive statistics, box plot, and diurnal plot as well as spatial analysis such as contour plot and wind rose diagram. Spearman correlation was used to identify the association of O₃ with its precursors and weather parameters. The results show that a higher concentration of O₃ during the weekend due to “ozone weekend effects” was pronounced, however, a slightly significant effect was observed in Perai. The two monsoonal seasons in Malaysia had a minimal effect on the study areas except for Kuala Terengganu due to the geographical location. The diurnal pattern of O₃ concentration indicates bimodal peaks of O₃ precursors during the peak traffic hours in the morning and evening with the highest intensity of O₃ precursors detected in Perai. Spearman correlation analysis determined that the variations in O₃ concentrations during day and nighttime generally coincide with the influence of nitrogen oxides (NO) and temperature. Lower NO concentration will increase the amount of O₃ concentration and an increasing amount of O₃ concentration is influenced by the higher temperature of its surroundings. Two predictive models, i.e., linear (multiple linear regression) and nonlinear models (artificial neural network) were developed and evaluated to predict the next day and nighttime O₃ levels. ANN resulted in better prediction for all areas with better prediction identified for daytime O₃ levels.

Feature selection is considered as one of the essential steps in data pre-processing. However, all of the previous studies on predicting PM₁₀ concentration in Malaysia have been limited to statistical method feature selection, and none of these studies used machine-learning approaches. Therefore, the objective of this research is to investigate the influence variables of the PM₁₀ prediction model by using wrapper feature selection to compare the prediction model performance of different wrapper feature selection and to predict the concentration of PM₁₀ for the next day. This research uses 10 years of daily data on pollutant concentrations from two stations (Klang and Shah Alam) obtained from the Department of Environment Malaysia (DOE) from 2009 until 2018. Six wrapper methods (forward selection, backward elimination, stepwise, brute-force, weight-guided and genetic algorithm evolution and the predictive analytics multiple linear regression (MLR) and artificial neural network (ANN)) were implemented in this study. This study found that brute-force is the dominant wrapper method in most of the best models in selecting important features for MLR. Moreover, compared to MLR, ANN provides more advantages regarding model accuracy and permits feature selection in predicting PM₁₀. The overall results revealed that the RMSE value for next day prediction in Klang is 20.728, while the AE value is 15.69. Furthermore, the RMSE value for next day prediction in Shah Alam is 10.004, while the AE value is 7.982. Finally, all of the predicted models in Klang and Shah Alam can be used to predict the PM₁₀ concentrations. This proposed model can be used as a tool for an early warning system in giving air quality information to local authorities in order to formulate air-quality-improvement strategies.

Former mining sites cause serious environmental problems worldwide as they are contaminated with hazardous levels of metals. Mined lands are characterized by a deserted landscape due to the lack of organic matter in soil. Research analyses confirmed that the structure of soil in abandoned surface mines has affected the occurrence of ecological processes and natural colonization of vegetation cannot take place. Moreover, phytoextraction of metals is possible only in soils with specific parameters. Previously conducted studies proposed the in situ supplementation with biochar, compost or agri-food wastes as a solution to the lack of organic carbon and nitrogen in areas affected by mining. Therefore, the main aim of this review is to investigate what improvements different organic amendments can bring to mining-impacted soils to support plant growth without affecting the bioavailability of metals. We concluded that contaminants are specific to the mining activity, while organic treatments cause the increase of soil pH, which influences, to the greatest extent, the bioavailability of metals.

Many invasive plant species use interactions with their anthropic environment as a propagation factor and benefit from climate changes, which have become accentuated in the last decade. The way such species interact with climate changes, as well as their high specific ecological plasticity, gives them a consistent advantage over native plant species. This work aims to demonstrate through a simple calculation the quantification of the productive potential of a wet meadow on which populations of an invasive plant species grew. The loss of productive potential induced by Reynoutria japonica Houtt on a mountain meadow in Ciocănești village, Romania, was the main objective. In the case of the productive potential of the meadows, a method for the general calculation of such losses was shown. The degree of anthropization of the studied area was also evaluated, correlating the degree of anthropization with the invasive species’ potential for spreading and affecting the mountain area.