Freely dissolved Organochlorine Pesticides (OCPs) and Polychlorinated Biphenyls (PCBs) along the Indus River Pakistan: Spatial pattern and Risk assessment

Freely dissolved OCPs and PCBs were measured by using polyethylene passive samplers at 15 sites during 2014 throughout the stretch of the Indus River to investigate the spatial pattern and risk assess. Levels (pg/L) of dissolved ∑OCPs and ∑PCBs ranged from 34 to 1600 and from 3 to 230. Among the detected OCPs, dissolved DDTs (p,p′-DDE, followed by p,p′-DDT) predominated with levels of 0.48 to 220 pg/L. The order of occurrence for other studied OCPs was as follows: HCB, endosulfans, chlordanes, and HCHs. Spatially, dissolved (pg/L) ∑OCPs varied (p \u3c 0.05) as the following: surface water of the alluvial riverine zone (ARZ) showed the highest levels (114) followed by the frozen mountain zone (FMZ) (52.9), low-lying zone (LLZ) (28.73), and wet mountain zone (WMZ) (14.43), respectively. However, our zone-wise PCB data did not exhibit significant differences (p \u3e 0.05). Principal component analysis/multilinear regression results showed pesticide usage in the crop/orchard fields and health sector, electric and electronic materials, and widespread industrial activities as the main source of OCPs and PCBs along the Indus River. Our results showed that OCPs and PCBs contaminated water intake, playing an important role towards the considerable cancer/non-cancer risk (HI and CR values) along the Indus River Flood-Plain

Time Series Analysis and Forecasting of Air Pollutants Based on Prophet Forecasting Model in Jiangsu Province, China

Due to recent developments in the global economy, transportation, and industrialization, air pollution is one of main environmental issues in the 21st century. The current study aimed to predict both short-term and long-term air pollution in Jiangsu Province, China, based on the Prophet forecasting model (PFM). We collected data from 72 air quality monitoring stations to forecast six air pollutants: PM10, PM2.5, SO2, NO2, CO, and O3. To determine the accuracy of the model and to compare its results with predicted and actual values, we used the correlation coefficient (R), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results show that PFM predicted PM10 and PM2.5 with R values of 0.40 and 0.52, RMSE values of 16.37 and 12.07 μg/m3, and MAE values of 11.74 and 8.22 μg/m3, respectively. Among other pollutants, PFM also predicted SO2, NO2, CO, and O3 with R values are between 5 μg/m3 to 12 μg/m3; and MAE values between 2 μg/m3 to 11 μg/m3. PFM has extensive power to accurately predict the concentrations of air pollutants and can be used to forecast air pollution in other regions. The results of this research will be helpful for local authorities and policymakers to control air pollution and plan accordingly in upcoming years