The goal of this research is to assess hazardous heavy metal levels in PM_2.5 fractioned road dust in order to quantify the risk of inhalation and potential health effects. To accomplish this, Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) was used to determine concentrations of eight heavy metals (Cr, Cu, Ni, Zn, Cd, As, Pb, and Hg) in the PM_2.5 portion of road dust samples from five different land use areas (commercial, residential, industrial, parks, and educational) in Zhengzhou, China. The following were the average heavy metal concentrations in the city: Cr 46.26 mg/kg, Cu 25.13 mg/kg, Ni 12.51 mg/kg, Zn 152.35 mg/kg, Cd 0.56 mg/kg, As 11.53 mg/kg, Pb 52.15 mg/kg, and Hg 0.32 mg/kg. Two pollution indicators, the Pollution Index (PI) and the Geoaccumulation Index (I_geo), were used to determine the degree of contamination. Both PI and I_geo indicated the extreme pollution of Hg and Cd, while PI also ranked Zn in the extreme polluted range. The US Environmental Protection Agency (USEPA) model for adults and children was used to estimate health risks by inhalation. The results identified non-carcinogenic exposure of children to lead (HI > 0.1) in commercial and industrial areas. Both children and adults in Zhengzhou’s commercial, residential, and park areas are exposed to higher levels of copper (Cu), lead (Pb), and zinc (Zn).

This paper investigates the spatiotemporal variability in hydrometeorological time-series to evaluate the current and future scenarios of water resources availability from upper Indus basin (UIB). Mann–Kendall and Sen’s slope estimator tests were used to analyze the variability in the temperature, precipitation, and streamflow time-series data at 27 meteorological stations and 34 hydrological stations for the period of 1963 to 2014. The time-series data of entire study period were divided into two equal subseries of 26 years each (1963–1988 and 1989–2014) to assess the overlapping aspect of climate change acceleration over UIB. The results showed a warming pattern at low altitude stations, while a cooling tendency was detected at high-altitude stations. An increase in streamflow was detected during winter and spring seasons at all hydrological stations, whereas the streamflow in summer and autumn seasons exhibited decreasing trends. The annual precipitation showed a significant decreasing trend at ten stations, while a significant increasing trend was observed at Kohat station during second subseries of the study period. The most significant winter drying trends were observed at Gupis, Chitral, Garidopatta, and Naran stations of magnitude of 47%, 13%, 25%, and 18%, respectively, during the second subseries. The annual runoff exhibited significant deceasing trends over Jhelum subbasin at Azad Pattan, Chinari, Domel Kohala, Muzaffarabad, and Palote, while within Indus basin at Chahan, Gurriala, Khairabad, Karora, and Kalam in the second time-series. It is believed that the results of this study will be helpful for the decision-makers to develop strategies for planning and development of future water resources projects.

Precise and reliable hydrological runoff prediction plays a significant role in the optimal management of hydropower resources. Nevertheless, the hydrological runoff practically possesses a nonlinear dynamics, and constructing appropriate runoff prediction models to deal with the nonlinearity is a challenging task. To overcome this difficulty, this paper proposes a three-stage novel hybrid model, namely, CVS (CEEMDAN-VMD-SVM), by coupling the support vector machine (SVM) with a two-stage signal decomposition methodology, combining complete ensemble empirical decomposition with additive noise (CEEMDAN) and variational mode decomposition (VMD), to obtain inclusive information of the runoff time series. Hydrological runoff data of the Swat River, Pakistan, from 1961 to 2015 were taken for prediction. CEEMDAN decomposes the runoff time series into subcomponents, and VMD performs further decomposition of the high-frequency component obtained after CEEMDAN decomposition to improve the prediction activity. Afterward, the SVM algorithm was applied to the decomposed subcomponents for the prediction purpose. Finally, four statistical indices are utilized to measure the performance of the CVS model compared with other hybrid models including CEEMDAN-VMD-MLP (multilayer perceptron), CEEMDAN-SVM, VMD-SVM, CEEMDAN-MLP, VMD-MLP, SVM, and MLP. The CVS model performs better during the training period by reducing RMSE by 71.28% and 40.06% compared with MLP and CEEDMAD-VMD-SVM models, respectively. However, during the testing period, the error reductions include RMSE by 68.37% and 35.33% compared with MLP and CEEDMAD-VMD-SVM models, respectively. The results highlight that the CVS model outperforms other models in terms of accuracy and error reduction. The research also highlights the superiority of other hybrid models over standalone in predicting the hydrological runoff. Therefore, the proposed hybrid model is applicable for the nonlinear features of runoff time series with feasibility for future planning and management of water resources.