资源环境科技发展态势分析平台

The main objective of the present study is to introduce public preferences into the development of water-quality criteria that effectively balance environmental concerns and socioeconomic values. A novel feature of our research is the analysis of subjective public judgments with Bayesian inference techniques, which are ultimately connected with environmental conditions through mathematical modeling. Our case study is the Bay of Quinte, Ontario, Canada; an embayment at the northeastern end of Lake Ontario with a long history of eutrophication, characterized by frequent and spatially extensive algal blooms and predominance of toxic cyanobacteria. In this study, we present a major survey to determine public opinions on water quality in the area. Our survey was conducted among a random sample of 1527 local residents and tourists during the summers of 2013 and 2014. The key findings of the survey were: (i) fishing (29%) and beauty of the area (20%) were the main reasons for public use of the Bay of Quinte; (ii) among different water-quality problems, the public chose the algal scums (26%) and the integrity of fish populations (22%) as the main issues; (iii) only 30% of the returning visitors noticed that the clarity of water is better now relative to the prevailing conditions five years ago; (iv) there is a dramatic change in public sentiment between the beginning and end of the summer season; and (v) a substantial portion of local residents were willing to contribute financially towards the restoration of the bay. Our modeling analysis suggests that the likelihood of public satisfaction increases significantly when the total phosphorus concentrations fall below the critical levels of 20-25 mu g L-1, which however is a difficult target to achieve even under significantly reduced nutrient-loading conditions. Other biological variables such as chlorophyll a concentrations, harmful algal blooms, and toxin levels in locations frequently used by the public appear to more closely influence their satisfaction level.