Comparison between odorous emission dispersion pattern and public risk perception near a Waste-to-Energy plant

Though Waste-to-Energy (WTE) facilities offer effective solutions to the Municipal Solid Waste (MSW) generating energy and reducing greenhouse gas emissions, however, the Not-In-My-Back-Yard (NIMBY) boycotts are increasingly becoming an obstruction of the sitting and operation of WTE facilities in developing countries. Today more than 100 WTE plants operate in China and approximately 400 new facilities are planned over the next decade. In this context, the East China Normal University has been carried out a survey of the public acceptability of WTE plants in Shanghai (China) for several years.. This study introduces insight into the relationship between air pollutant (especially odorous substances) dispersion pattern and public risk perception, taking Laogang WTE Plant (in Shanghai) as a case study. The poor performance of waste sorting leads to odour emission, and therefore it meets with strong adverse attitude from the local residents - who tend to associate odours to unhealthy conditions. In the study, firstly odorous substances were specified through literature survey, and then CALPUFF model was applied to simulate dispersion of the air emissions of the incineration within a semicircular buffer zone with a radius of 10 km. The meteorological data of Shanghai in 2013 was applied as meteorological background. The most sensitive areas to the odour emission around WTE facility were identified at this step. During a previous questionnaire survey, the nearby residents’ concerns and attitudes surrounding the Laogang WTE Plant were assessed. A Geographic information System techniques were applied to this survey to conduct the spatial analysis of physical perceptions, especially odour annoyance and opposition to WTE facilities. Therefore, in the second step, a correspondence analysis between the distribution of air emission and public risk perceptions was executed. Based on these results, a point-to-point comparison was executed with statistic analysis to gain the comparison and accordance of air emission and public risk perception. The results will be benefit to risk communication and environment education to reduce conflict and to build social trust.