可生物降解塑料取代传统塑料--对碳排放的影响

In recent years,a great deal of attention has been focused on the environmental impact of plastics,includ-ing the carbon emissions related to plastics,which has promoted the application of biodegradable plas-tics.Countries worldwide have shown high interest in replacing traditional plastics with biodegradable plastics.However,no systematic comparison has been conducted on the carbon emissions of biodegrad-able versus traditional plastic products.This study evaluates the carbon emissions of traditional and biodegradable plastic products(BPPs)over four stages and briefly discusses environmental and economic perspectives.Four scenarios-namely,the traditional method,chemical recycling,industrial composting,and anaerobic digestion-are considered for the disposal of waste BPPs(WBPPs).The analysis takes China as a case study.The results show that the carbon emissions of 1000traditional plastic products(plastic bags,lunch boxes,cups,etc.)were52.09-150.36 carbon emissions equivalent of per kilogram(kg CO_(2)eq),with the stage of plastic production contributing 50.71%-50.77%.In comparison,1000 similar BPPs topped out at 21.06-56.86 kg CO_(2)eq,approximately 13.53%-62.19%lower than traditional plastic prod-ucts.The difference was mainly at the stages of plastic production and waste disposal,and the BPPs showed significant carbon reduction potential at the raw material acquisition stage.Waste disposal plays an important role in environmental impact,and composting and anaerobic digestion are considered to be preferable disposal methods for WBPPs.However,the high cost of biodegradable plastics is a challenge for their widespread use.This study has important reference significance for the sustainable development of the biodegradableplastics industry.

Mercury levels and estimated total daily intakes for children and adults from an electronic waste recycling area in Taizhou, China: Key role of rice and fish consumption

In order to assess the potential health risks of Hg pollution, total mercury（T–Hg） and methyl mercury（Me Hg） concentrations were determined in air, dust, surface soil, crops, poultry,fish and human hair samples from an electronic waste（e-waste） recycling area in Taizhou,China. High concentrations of T–Hg and Me Hg were found in these multiple matrices, and the mean concentration was 30.7 ng/m3 of T–Hg for atmosphere samples, 3.1 μg/g of T–Hg for soil, 37.6 μg/g of T–Hg for dust, 20.3 ng/g of Me Hg for rice and 178.1 ng/g of Me Hg for fish,suggesting that the e-waste recycling facility was a significant source of Hg. The inorganic Hg（I–Hg） levels（0.84 μg/g） in hair samples of e-waste workers were much higher than that in the reference samples. Pearson＇s correlation coefficients showed that strong positive correlations（p 〈 0.01） between hair I–Hg and time staying in industrial area（r = 0.81） and between Me Hg and fish consumption frequency（r = 0.91）, imply that workers were mainly exposed to Hg vapor through long-time inhalation of contaminated air and dust, while other population mainly exposed to Me Hg through high-frequency fish consumption. The estimated daily intakes of Hg showed that dietary intake was the major Hg exposure source,and Hg intakes from rice and fish were significantly higher than from any other foods. The estimated total daily intakes（TDIs） of Me Hg for both children（696.8 ng/（kg·day）） and adults（381.3 ng/（kg·day）） greatly exceeded the dietary reference dose（Rf D） of 230 ng/（kg·day）,implying greater health risk for humans from Hg exposures around e-waste recycling facilities.

Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load

To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs,a novel modification of a conventional model predictive control is proposed.The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty.By modelling a probabilistic dependence among flow,load,and electricity tariffs,the expected cost function is obtained and used in the constrained optimization.The proposed control strategy explicitly incorporates the cycling nature of customer load.Furthermore,for daily cycling load,a fixed-end time and a fixed-end output problem are addressed.It is demonstrated that the proposed control strategy is a convex optimization problem.While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations.Also,the expected cost across the flow variations is considered.The density function of load probability improves load prediction over a progressive prediction horizon,and a nonlinear battery model is utilized.