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Health risk assessment of municipal solid waste incineration emissions based on regression analysis
Zhuoshi Huang1,2, Jicui Cui1,3, Abdoulaye Boré1
1School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China.
Abstract:
This study examined the potential health risks posed by the operation of 96 waste-to-energy (WtE) plants in 30 cities in the Bohai Rim of China. Utilizing a sophisticated simulation approach, the Weather Research and Forecasting (WRF) model coupled with the California Puff (CALPUFF) model, we obtained the spatial distribution of pollutants emitted by WtE plants in the atmosphere. Hazard indices (HI) and cancer risks (CR) were calculated for each plant using the United States Environmental Protection Agency's recommended methodologies. The results indicated that both HIs and CRs were generally low, with values below the accepted threshold of 1.0 and 1.0 × 10-6, respectively. Specifically, the average HI and CR values for the entire study area were 2.95 × 10-3 and 3.43 × 10-7, respectively. However, some variability in these values was observed depending on the location and type of WtE plant. A thorough analysis of various parameters, such as waste composition, moisture content, and operating conditions, was conducted to identify the factors that influence the health risks associated with incineration. The findings suggest that proper waste sorting and categorization, increased cost of construction, and elevated height of chimneys are effective strategies for reducing the health risks associated with incineration. Overall, this study provides valuable insights into the potential health risks associated with WtE plants in the Bohai Rim region of China. The findings can serve as useful guidelines for law enforcement wings and industry professionals seeking to minimize the risks associated with municipal solid waste (MSW) management and promote sustainable development.
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In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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