With the continuous improvement of people's living standards and their increasing demand for fresh food, the cold chain logistics industry has developed rapidly. One of the biggest challenges to today's cold chain logistics is to offer fresh food while minimizing CO 2 emissions. The fresh degree and CO 2 emissions are involved in the vehicle routing optimization problem in the cold chain logistics. In order to meet the quality requirement for fresh agricultural products and low carbon logistics, a novel routing optimization model considering the costs of quality deterioration and carbon emissions (Low Carbon and Freshness Degrees Vehicle Routing Problem (LCFD-VRP)) for cold chain distribution was established in this study. This model takes into account the fixed cost, fuel cost and time window penalty cost. An improved ant colony algorithm (IACA) is used to optimize the whole vehicle distribution routing with its strong global search ability. Tabu Search (TS) algorithm is used to search the single vehicle distribution routing with its good local search ability. An IACA combined with TS (IACATS) was proposed to solve the above LCFD-VRP model. The practicability of the model and the effectiveness of the above improved algorithm are verified using a real case study. The results of Zhoushan Dayang Refrigerated Logistics Co., Ltd. showed that, compared with the traditional algorithm, IACATS could reduce the dispatching of two refrigerated vehicles, thus lowering the total cost by 4.94%, shortening the actual transportation distance by 5.50% and cutting the total CO 2 emissions by 8.9%. Therefore, the LCFD-VRP model can effectively help to achieve the low carbon emissions, multi-variety and low-cost distribution of fresh agricultural products. The proposed model and IACATS algorithm would be used to optimize VRP in cold chain enterprises. The results of this study also provide management suggestions for cold chain enterprises to effectively balance economic cost and environmental cost.Sustainability 2019, 11, 6584 2 of 22 has higher requirements for quality and timeliness [2]. The normal operation of refrigeration equipment produces a large amount of CO 2 emissions and other greenhouse gases generated from distribution vehicles in the process of delivery. It leads to an increase in greenhouse gases and, as a result, air pollution and greenhouse effect will be intensified. The increased carbon emissions not only pollute the environment, but also raise the cost of logistics enterprises due to the implementation of national carbon-taxing policies [3]. Therefore, how to reduce carbon emissions in cold chain logistics, thereby alleviating the global warming caused by the greenhouse effect, has become a popular issue in the research field of cold chain logistics distribution routes [4].In recent years, the optimization of cold chain logistics distribution routes has attracted extensive academic attention [5,6]. Although the above-mentioned studies have considered the impacts of various cost factors on cold chain logistics, t...

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Samples of atmospheric depositions from five types of functional areas in Shijiazhuang, Hebei Province, China, were collected, and the concentrations of six toxic heavy metals (Cd, Cr, Cu, Pb, Ni, and Zn) were measured. Geographic information system, Pb isotope assessment, multivariate statistical analysis (principal component analysis, PCA), the geoaccumulation index (Igeo), potential ecological risk index (PERI), and a health risk assessment model were used to study the degree of pollution, identify sources of pollution, and assess the health risks to children and adults via three pathways (hand–mouth intake, skin contact, and respiration). The results show that the high traffic volume and exhaust gas emissions have led to high concentrations of heavy metals. The Igeo and PERI values of Cd (0.38–2.0 and 108–4531, respectively), indicating the present high pollution level and potential risk, respectively, varied the most. Pb isotope and PCA showed that Pb, Zn, and Cd from atmospheric deposition come from power plants and traffic—Cu is related to traffic, and Ni and Cr come mainly from soil particles (natural source). The health risk assessment showed that heavy metals in atmospheric depositions are at a safe level in the study area.

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Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up ofLow,R, andUp, which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameterscandgfor SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.