Qualitative and quantitative analyses of derivatized phenols in Beijing and in Xinglong were performed from 2016 to 2017 using gas chromatography-mass spectrometry.The results showed substantially more severe pollution in Beijing.Of the 14 compounds detected,the total average concentration was 100 ng/m~3 in Beijing,compared with 11.6 ng/m~3 in Xinglong.More specifically,concentration of nitro-aromatic compounds(NACs)(81.9 ng/m~3 in Beijing and 8.49 ng/m3 in Xinglong) was the highest,followed by aromatic acids(14.6 ng/m~3 in Beijing and 2.42 ng/m~3 in Xinglong) and aromatic aldehydes(3.62 ng/m~3 in Beijing and 0.681 ng/m~3 in Xinglong).In terms of seasonal variation,the highest concentrations were found for 4-nitrocatechol in winter in Beijing(79.1±63.9 ng/m~3) and 4-nitrophenol in winter in Xinglong(9.72±8.94 ng/m~3).The analysis also revealed diurnal variations across different seasons.Most compounds presented higher concentrations at night in winter because of the decreased boundary layer height and increased heating intensity.While some presented higher levels during the day,which attributed to the photo-oxidation process for summer and more biomass burning activities for autumn.Higher concentrations appeared in winter and autumn than in spring and summer,which resulted from more coal combustions and adverse meteorological conditions.The significant correlations among NACs indicated similar sources of pollution.Higher correlations presented within each subgroup than those between the subgroups.Good correlations between levoglucosan and nitrophenols,nitrocatechols,nitro salicylic acids,with correlation coefficients(r) of 0.66,0.69 and 0.69,respectively,indicating an important role of biomass burning among primary sources. 相似文献
Electric vehicles based on lithium-ion batteries (LIB) have seen rapid growth over the past decade as they are viewed as a cleaner alternative to conventional fossil-fuel burning vehicles, especially for local pollutant (nitrogen oxides [NOx], sulfur oxides [SOx], and particulate matter with diameters less than 2.5 and 10 μm [PM2.5 and PM10]) and CO2 emissions. However, LIBs are known to have their own energy and environmental challenges. This study focuses on LIBs made of lithium nickel manganese cobalt oxide (NMC), since they currently dominate the United States (US) and global automotive markets and will continue to do so into the foreseeable future. The effects of globalized production of NMC, especially LiNi1/3Mn1/3Co1/3O2 (NMC111), are examined, considering the potential regional variability at several important stages of production. This study explores regional effects of alumina reduction and nickel refining, along with the production of NMC cathode, battery cells, and battery management systems. Of primary concern is how production of these battery materials and components in different parts of the world may impact the battery’s life cycle pollutant emissions and total energy and water consumption. Since energy sources for heat and electricity generation are subject to great regional variation, we anticipated significant variability in the energy and emissions associated with LIB production. We configured Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET®) model as the basis for this study with key input data from several world regions. In particular, the study examined LIB production in the US, China, Japan, South Korea, and Europe, with details of supply chains and the electrical grid in these regions. Results indicate that 27-kWh automotive NMC111 LIBs produced via a European-dominant supply chain generate 65 kg CO2e/kWh, while those produced via a Chinese-dominant supply chain generate 100 kg CO2e/kWh. Further, there are significant regional differences for local pollutants associated with LIB, especially SOx emissions related to nickel production. We find that no single regional supply chain outperforms all others in every evaluation metric, but the data indicate that supply chains powered by renewable electricity provide the greatest emission reduction potential.