Oily sludge is a hazardous solid waste, hazardous ingredients (hazardous elements and heavy metals) in pyrolysis products have the potential for secondary pollution. The distribution of hazardous elements (N/S/Cl) during the pyrolysis of oily sludge was determined at different pyrolysis temperatures (350–750°C), and the potential ecological risk of heavy metals (Zn/Cu/Cd/Cr/Pb) in the solid residue was evaluated. The results showed that N-pollutants in oily sludge were mainly distributed at higher pyrolysis temperatures (550°C), S and Cl-pollutants were mainly distributed at 450°C. The leaching concentration of Zn metal in oily sludge was 119.73 mg/L, which has exceeded the concentration limit (100 mg/L) stipulated by GB 16,889–2008. The pyrolysis treatment could reduce the leaching rate of Zn metal to 18.39 mg/L Moreover, the risk assessment code (RAC) values for Zn showed a very high risk environmental risk level. Pyrolysis treatment could reduce the RAC value of Zn and change the environmental risk level of Zn to low risk. The results of this study provide a theoretical basis for the pyrolysis treatment of oily sludge and the control of harmful ingredients.
Heat wave is serious natural disaster that can harm human health and affect social economy, transportation and ecological environment. This paper investigates the long term trends of high temperature events in three major cities (Beijing, Tianjin and Shijiazhuang) of northern China during 1970–2019, and quantifies the contributions of main influencing factors to the variability of high temperature days. High temperature events in Beijing–Tianjin–Shijiazhuang cities mainly occur from June to July and account for 60–65% of the annual total, showing a significant upward trend in the interannual change. There is a trend of high temperature events starting early and ending late in recent 50 years, and this trend is intensified by the increasing urbanization. Due to geographical location and city scale, the frequency and intensity of high temperature events show difference among Beijing, Tianjin and Shijiazhuang cities. Variance analysis shows that climate warming contributes 20.2–25.5% to the variability of high temperature days, while urban heat island accounts for 14.7–24.2%, equal to the sum of the contributions from atmospheric circulation and solar activity.
Global climate change is significantly altering the energy consumption patterns and outdoor environments of buildings. The current meteorological data utilized for building design exhibit numerous deficiencies. To effectively address the needs of future building usage in design, it is crucial to establish more refined meteorological parameters that accurately reflect the climate of specific geographical locations. Utilizing 60 years of meteorological data from Guangzhou, this study employs the cumulative distribution functions (CDFs) method to define four archetypal meteorological years, providing a robust foundation for subsequent analysis. The findings indicate a significant increase in the frequency of high temperatures and temperature values during the summer months, with an increase of nearly 20% in the cumulative degree hours (CDHs) used for calculating a typical meteorological year (TMY4) over the past 30 years. Additionally, there has been an increase of 0.4–0.7 °C in the air conditioning design daily temperature. The statistics on outdoor calculation parameters for different geographical locations, as well as outdoor design parameters for varying guaranteed rate levels in the Pearl River Delta, reveal a substantial impact on outdoor calculation parameters. The maximum difference in cooling load is approximately 9.3%, with a generally high cooling demand in summer and a relatively low heating demand in winter. Furthermore, the calculation values for different non-guaranteed rates can be applied flexibly to meet the needs of engineering applications. This study provides a valuable reference for updating meteorological parameters in building design. By refining meteorological parameters, this study enables more accurate predictions of energy needs, leading to optimized building designs that reduce energy consumption and greenhouse gas emissions. It supports the development of resilient buildings capable of adapting to changing climatic conditions, thus contributing to long-term environmental sustainability.
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