Spatial distribution and health risks assessment of heavy metals in e-waste dumping sites from Pakistan.

The present study focused on to determine the concentration and health risk of heavy metals (Cu, Pb, Zn, Cd, Hg, Cr) in e-waste contaminated soils collected from different provinces of Pakistan. Further, the impact of heavy metals on soil enzyme activities and microbial community was also investigated. The concentration (mg/kg) of Hg, Zn, Fe, Cu, Pb, Cd, and Cr ranged between 0-0.258, 2.284-6.587, 3.005-40.72, 8.67-36.88, 12.05-35.03, 1.03-2.43, and 33.13-60.05, respectively. The results revealed that Lahore site of Punjab province indicated more concentration of heavy metals as compared to other sites. The level of Cr at all sites whereas Hg at only two sites exceeds the World Health Organization standards (WHO) for soil. Soil enzyme activity exhibited dynamic trend among the sites. Maximum enzyme activity was observed for urease followed by phosphatase and catalase. Contamination factor (Cf), Pollution load index (PLI), and geo-accumulation index (Igeo) results showed that all the sites are highly contaminated with Cu, Cd, and Pb. Hazard index (HI) was less than 1 for children and adults suggesting non-carcinogenic health risk. Principle component analysis results depicted relation among Cr, Fr, catalase, and actinomycetes; Cd, OM, urease, and bacteria, and Pb, Cu, Zn, Hg, and phosphatase, suggesting soil enzymes and microbial community profiles were influenced by e-waste pollution. Therefore, there is a dire need to introduce sustainable e-waste recycling techniques as well as to make stringent e-waste management policies to reduce further environmental contamination.

Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China.

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.

Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach.

The novel coronavirus (COVID-19), first identified at the end of December 2019, has significant impacts on all aspects of human society. In this study, we aimed to assess the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta (YRD) region using a random forest (RF) model. To estimate the accuracy of the model, the cross-validation (CV), determination coefficient R2, root mean squared error (RMSE) and mean absolute error (MAE) were used. The results demonstrate that the RF model achieved the best performance in the prediction of PM10 (R2 = 0.78, RMSE = 8.81 µg/m3), PM2.5 (R2 = 0.76, RMSE = 6.16 µg/m3), SO2 (R2 = 0.76, RMSE = 0.70 µg/m3), NO2 (R2 = 0.75, RMSE = 4.25 µg/m3), CO (R2 = 0.81, RMSE = 0.4 µg/m3) and O3 (R2 = 0.79, RMSE = 6.24 µg/m3) concentrations in the YRD region. Compared with the prior two years (2018-19), significant reductions were recorded in air pollutants, such as SO2 (-36.37%), followed by PM10 (-33.95%), PM2.5 (-32.86%), NO2 (-32.65%) and CO (-20.48%), while an increase in O3 was observed (6.70%) during the COVID-19 period (first phase). Moreover, the YRD experienced rising trends in the concentrations of PM10, PM2.5, NO2 and CO, while SO2 and O3 levels decreased in 2021-22 (second phase). These findings provide credible outcomes and encourage the efforts to mitigate air pollution problems in the future.

Evaluating the impact of roads on the diversity pattern and density of trees to improve the conservation of species.

Roadside trees alter biotic and abiotic factors of plants diversity in an ecosystem. Rows of plants grow along the roadside due to the interplay between the arrival of propagule and seedling establishment, which depends on the road's specifications, land pattern, and road administration and protection practices. A field study was conducted to measure the roadside tree diversity in the city of Karachi (Pakistan). A total of 180 plots, divided into three primary road groups, were surveyed. The highest quantity of tree biomass per unit area was found on wide roads, followed by medium roads. On narrow roads, the least biomass was detected. A single species or a limited number of species dominated the tree community. Conocarpus erectus was the most dominant non-native species on all types of sidewalks or roadsides, followed by Guaiacum officinale. A total of 76 species (32 non-natives and 44 natives) that were selectively spread along the roadsides of the city were studied. There was a significant difference in phylogenetic diversity (PD), phylogenetic mean pairwise distance (MPD), and phylogenetic mean nearest taxon distance (MNTD) among wide, medium, and narrow roads. Management practices have a significant positive correlation with diversity indices. Our study identified patterns of diversity in roadside trees in Karachi. It provides the basis for future planning for plant protection, such as the protection of plant species, the maintenance of plant habitats, and the coordination of plant management in Karachi.

PCB118-Induced Cell Proliferation Mediated by Oxidative Stress and MAPK Signaling Pathway in HELF Cells.

The present study used human lung fibroblast (HELF) cells as a test model to evaluate the role of oxidative stress (OS) and extracellular signal-regulated kinases 1/2 (ERK1/2) protein in HELF cell proliferation exposed to PCB118. Results from 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide demonstrated that PCB118 at lower concentrations stimulated proliferation of HELF cell and abrogate proliferative effect at higher dose concentrations and in a time-dependent manner. Moreover, reactive oxygen species, malondialdehyde (MDA), and superoxide dismutase showed a significant increase at higher concentrations of PCB118 than the lower concentrations with the passage of time. Antioxidant enzymes such as glutathione peroxidase exhibited decreasing trends in dose- and time-dependent manner. Lipid peroxidation assay resulted in a significant increase in MDA level in PCB118-treated HELF cells compared with controls, suggesting that OS plays a key role in PCB118-induced toxicity. Comet assay indicated a significant increase in genotoxicity at higher concentrations of PCB118 exposure than the lower concentrations. It was found that PCB118 showed expression of ERK1/2 protein after 4 hours, while after 48 hours, the protein expression was less, indicating PCB toxicity to MAPK protein of HELF cell. Oxidative stress, ERK1/2, and HELF cell proliferation exhibited correlation. The results will elaborate toxicological evaluation of PCB118 to HELF cells and will help to develop drug for PCB-induced diseases.