To investigate the heavy metal and metalloid contamination of soil around a Huanan uranium tailings pond, abandoned in 1998, we defined a study area of 41.25 km² by a natural boundary and targeted 5 elements’ (U, Mn, As, Pb, Cr) single contamination and comprehensive pollution as the assessment contents. First, we collected 205 samples and evaluated them with the contamination factor (CF) method aiming at judging whether the single target element concentration exceeded the local background value and environmental quality standard. We obtained CF₁ (the background value of a certain target element as the baseline value) and CF₂ (the environmental quality standard for soils as the baseline value). Second, we evaluated the ecological risk of the key pollutant U with the risk assessment code (RAC) method, taking the 27 samples whose CF₂ > 1 as examples and concluded that the environmental risk of U was relatively high and should arouse concern. Third, we selected comprehensive pollution index (CPI) to assess the compound pollution degree of five target elements. Fourth, we constructed the U contamination and CPI’s continuous distribution maps with spatial interpolation, from which we worked out the sizes and positions of slightly, moderately and strongly polluted zones. Finally, we analyzed the spatial variability of U and CPI with the aid of a geostatistical variogram. We deduced that the spatial variation of uranium was in close relationship with local topography, and probably precipitation was the driving force of U contamination diffusion, whereas CPI exhibited weak spatial dependence with random characteristics. The above work showed that 3.14 km² soil near the pond was fairly seriously polluted, and the other 4 elements’ single contaminations were less serious, but the 5 target elements’ cumulative pollution could not be ignored; there were other potential pollution sources besides the uranium tailings pond. Some emergency measures should be taken to treat U pollution, and bioremediation is recommended, taking account into U’s high bioavailability. Further, special alerts should be implemented to identify the other pollution sources.

In this paper, the process of forecasting household energy consumption is studied within the framework of the nonparametric Gaussian Process (GP), using multiple short time series data. As we begin to use smart meter data to paint a clearer picture of residential electricity use, it becomes increasingly apparent that we must also construct a detailed picture and understanding of consumer’s complex relationship with gas consumption. Both electricity and gas consumption patterns are highly dependent on various factors, and the intricate interplay of these factors is sophisticated. Moreover, since typical gas consumption data is low granularity with very few time points, naive application of conventional time-series forecasting techniques can lead to severe over-fitting. Given these considerations, we construct a stacked GP method where the predictive posteriors of each GP applied to each task are used in the prior and likelihood of the next level GP. We apply our model to a real-world dataset to forecast energy consumption in Australian households across several states. We compare intuitively appealing results against other commonly used machine learning techniques. Overall, the results indicate that the proposed stacked GP model outperforms other forecasting techniques that we tested, especially when we have a multiple short time-series instances.

Investigating Nutrient Input from Rivers/Streams and Wastewater Treatment Plants to Utah Lake

Utah Lake is one of the largest natural freshwater lakes in the western United States. Its watershed is 9,800 km2. Utah Lake is located in Utah County which is expected to have the highest population growth in the state through 2060. Land use and water regulation has shifted the Utah Lake shoreline since the 1900s. Monitoring the land use and land cover change (LULCC) in the watershed is critical to understanding surrounding hydrology and future sustainability. In this study, we compared the Utah Lake shoreline change from 1953-2014 and classified the land cover in the Utah Lake watershed from 1985-2018. Our results show that there was a 41.45 km2  decrease in lake surface from 1953 to 2014. The shoreline around the Provo Bay and Goshen Bay has receded lake-ward considerably in 2014 compared to the 1953 shoreline, and the lost water and wetland area was equivalent to 3,851 football fields in size. Land cover change calculations indicate that from 1985 to 2018 urbanization increased by 6%, forest by 2%, and barren by 3%, whereas water and agriculture decreased by 1% and 6%, respectively. The findings from this project could be used by Utah’s legislature to implement meaningful watershed planning and management, especially in light of the state considering House Bill 272 that promotes “comprehensive restoration of Utah Lake by building an island on it.” The bill proposes an island in Utah Lake which could dramatically alter LULCC around the lake. In addition, any significant LULCC on and around the lake will modify the lake water budget, its ecosystem, and have profound consequences on Utah Lake watershed and the surrounding regions.

Since the Wenchuan earthquake in 2008, Old Beichuan County-town has experienced significant subsidence due to the disruption of the geological environment and the concurrent increase in precipitation. The ongoing land surface deformation poses a threat to the preservation and utilization of the Beichuan National Earthquake Ruins Museum (BNERM), as well as to the safety of urban residents’ lives. However, the evolutionary characteristics of surface deformation in these areas remain largely unexplored. Here, we focused on the BNERM control zone and employed the small-baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique to accurately measure land surface deformation and its spatiotemporal changes. Subsequently, we integrated this data with land cover types and precipitation to investigate the driving factors of deformation. The results indicate a slight overall elevation increase in the study area from June 2015 to May 2023, with deformation rates varying between −35.2 mm/year and 22.9 mm/year. Additionally, four unstable slopes were identified within the BNERM control zone. Our analysis indicates that surface deformation in the study area is closely linked to changes in land cover types and precipitation, exhibiting a seasonal cumulative pattern, and active geological activity may also be a cause of deformation. This study provides invaluable insights into the surface deformation characteristics of the BNERM and can serve as a scientific foundation for the protection of earthquake ruins, risk assessment, early warning, and disaster prevention measures.