High-Resolution PM2.5 Estimation Based on the Distributed Perception Deep Neural Network Model

Spatiotemporal Distribution Characteristics and Influencing Factors Analysis of Reference Evapotranspiration in Beijing–Tianjin–Hebei Region from 1990 to 2019 under Climate Change

Reference evapotranspiration (ET0) is an important part of the water and energy cycles during crop growth. Understanding the influencing factors and spatiotemporal variations of ET0 is of positive significance for guiding regional water-saving irrigation and regulating agricultural production. Data for daily meteorological observations of temperature, relative humidity, wind speed, and sunshine hours from 40 surface meteorological stations and the methods of climate tendency rate, Morlet wavelet, M-K mutation, path analysis, sensitivity analysis, and contribution rate analysis were utilized, to analyze the spatiotemporal distribution characteristics and influencing factors in the Beijing–Tianjin–Hebei region from 1990 to 2019. The ET0 from 1990 to 2019 was 958.9 mm, and there was a significant downward trend in the climate tendency rate of −3.07 mm/10 a. The ET0 presents a spatial distribution pattern decreasing from southwest to northeast. A change in the Beijing–Tianjin–Hebei region’s interannual ET0 occurred in 2016, with a decrease of 41.12 mm since then. The ET0 was positively correlated with temperature, wind speed, and sunshine hours, and negatively correlated with relative humidity; among those, wind speed and temperature are the dominant factors affecting the change of ET0. This study provides a scientific basis for the regulation and control of agricultural production in the Beijing–Tianjin–Hebei region.

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 × 10-7 to 0.070276. The GW models recorded a range from 5 × 10-8 to 0.045373, all of which were approaching the ideal MSE value of 0. Kling-Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden's connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data.