Shanghai, as one of the megacities and economic centers of China, is facing critical water quality challenges. This study analyzed the impact of urbanization on the water quality in Shanghai, from 2007 to 2015, using remote sensing (RS) and geographic information system (GIS) techniques. Five measurements of water quality were employed: total discharged wastewater volume, general water quality levels, dissolved oxygen (DO), permanganate, and ammonia nitrogen. The impacts of urban land-use changes on water quality were examined. An urban index was extracted from satellite image classification and was used to quantify the anthropogenic activities. In the watershed level, unit watersheds were delineated from topography and stream segments. Results showed that the primary contributors of water quality degradation in Shanghai were DO and ammonia nitrogen. Both indicators expressed clear seasonal patterns that can be explained by agricultural activities and urbanization processes in Shanghai during the study period. Water quality was also regulated through water use policies. For example, the degraded water quality in suburban outskirts and improved water quality was achieved through the enforced wastewater discharge regulations in central Shanghai. Analytical findings provide spatially explicit information for governmental management on protecting water resources and controlling wastewater emissions, thus, improving the quality of living environments in this ever-growing megacity.

Maps of the distribution of medically-important ticks throughout the US remain lacking in spatial and temporal resolution in many areas, leading to holes in our understanding of where and when people are at risk of tick encounters, an important baseline for informing public health response. In this work, we demonstrate the use of Bayesian Experimental Design (BED) in planning spatiotemporal surveillance of disease vectors. We frame survey planning as an optimization problem with the objective of identifying a calendar of sampling locations that maximizes the expected information regarding some goal. Here we consider the goals of understanding associations between environmental factors and tick presence and minimizing uncertainty in high risk areas. We illustrate our proposed BED workflow using an ongoing tick surveillance study in South Carolina parks. Following a model comparison study based on two years of initial data, several techniques for finding optimal surveys were compared to random sampling. Two optimization algorithms found surveys better than all replications of random sampling, while a space-filling heuristic performed favorably as well. Further, optimal surveys of just 20 visits were more effective than repeating the schedule of 111 visits used in 2021. We conclude that BED shows promise as a flexible and rigorous means of survey design for vector control, and could help alleviate pressure on local agencies by limiting the resources necessary for accurate information on arthropod distributions. We have made the code for our BED workflow publicly available on Zenodo to help promote the application of these methods to future surveillance efforts.